def __init__(self, data, vocab, training=True):
dir = get_download_dir()
_url = _get_dgl_url('dataset/jtnn.zip')
zip_file_path = '{}/jtnn.zip'.format(dir)
download(_url, path=zip_file_path)
extract_archive(zip_file_path, '{}/jtnn'.format(dir))
print('Loading data...')
if data in ['train', 'test']:
# ZINC subset
data_file = '{}/jtnn/{}.txt'.format(dir, data)
else:
# New dataset
data_file = data
with open(data_file) as f:
self.data = [line.strip("\r\n ").split()[0] for line in f]
self.vocab = vocab
print('Loading finished')
print('\t# samples:', len(self.data))
self.training = training
self.atom_featurizer_enc = get_atom_featurizer_enc()
self.bond_featurizer_enc = get_bond_featurizer_enc()
self.atom_featurizer_dec = get_atom_featurizer_dec()
self.bond_featurizer_dec = get_bond_featurizer_dec()
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./tox21_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/tox21.csv.gz'
data_path = get_download_dir() + '/tox21.csv.gz'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
df = pd.read_csv(data_path)
self.id = df['mol_id']
df = df.drop(columns=['mol_id'])
self.load_full = False
super(Tox21, self).__init__(df,
smiles_to_graph,
node_featurizer,
edge_featurizer,
"smiles",
cache_file_path,
load=load,
log_every=log_every,
n_jobs=n_jobs)
self.id = [self.id[i] for i in self.valid_ids]
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./sider_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/sider.zip'
data_path = get_download_dir() + '/sider.zip'
dir_path = get_download_dir() + '/sider'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/sider.csv')
super(SIDER, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
load=load,
log_every=log_every,
init_mask=True,
n_jobs=n_jobs)
def __init__(self, hidden_size, latent_size, depth, vocab_file=None):
super(DGLJTNNVAE, self).__init__()
if vocab_file is None:
default_dir = get_download_dir()
vocab_file = '{}/jtvae/{}.txt'.format(default_dir, 'vocab')
zip_file_path = '{}/jtvae.zip'.format(default_dir)
download(_get_dgl_url('dataset/jtvae.zip'), path=zip_file_path)
extract_archive(zip_file_path, '{}/jtvae'.format(default_dir))
with open(vocab_file, 'r') as f:
self.vocab = Vocab([x.strip("\r\n ") for x in f])
self.hidden_size = hidden_size
self.latent_size = latent_size
self.depth = depth
self.embedding = nn.Embedding(self.vocab.size(), hidden_size)
self.mpn = DGLMPN(hidden_size, depth)
self.jtnn = DGLJTNNEncoder(self.vocab, hidden_size, self.embedding)
self.decoder = DGLJTNNDecoder(self.vocab, hidden_size,
latent_size // 2, self.embedding)
self.jtmpn = DGLJTMPN(hidden_size, depth)
self.T_mean = nn.Linear(hidden_size, latent_size // 2)
self.T_var = nn.Linear(hidden_size, latent_size // 2)
self.G_mean = nn.Linear(hidden_size, latent_size // 2)
self.G_var = nn.Linear(hidden_size, latent_size // 2)
self.atom_featurizer_enc = get_atom_featurizer_enc()
self.bond_featurizer_enc = get_bond_featurizer_enc()
self.atom_featurizer_dec = get_atom_featurizer_dec()
self.bond_featurizer_dec = get_bond_featurizer_dec()
def __init__(self, hidden_size, latent_size, depth, vocab=None, vocab_file=None):
super(DGLJTNNVAE, self).__init__()
if vocab is None:
if vocab_file is None:
default_dir = get_download_dir()
vocab_file = '{}/jtnn/{}.txt'.format(default_dir, 'vocab')
zip_file_path = '{}/jtnn.zip'.format(default_dir)
download(_get_dgl_url('dataset/jtnn.zip'), path=zip_file_path)
extract_archive(zip_file_path, '{}/jtnn'.format(default_dir))
self.vocab = Vocab([x.strip("\r\n ") for x in open(vocab_file)])
else:
self.vocab = vocab
self.hidden_size = hidden_size
self.latent_size = latent_size
self.depth = depth
self.embedding = nn.Embedding(self.vocab.size(), hidden_size)
self.mpn = DGLMPN(hidden_size, depth)
self.jtnn = DGLJTNNEncoder(self.vocab, hidden_size, self.embedding)
self.decoder = DGLJTNNDecoder(
self.vocab, hidden_size, latent_size // 2, self.embedding)
self.jtmpn = DGLJTMPN(hidden_size, depth)
self.T_mean = nn.Linear(hidden_size, latent_size // 2)
self.T_var = nn.Linear(hidden_size, latent_size // 2)
self.G_mean = nn.Linear(hidden_size, latent_size // 2)
self.G_var = nn.Linear(hidden_size, latent_size // 2)
self.n_nodes_total = 0
self.n_passes = 0
self.n_edges_total = 0
self.n_tree_nodes_total = 0
def __init__(self, file_path=None):
if file_path is None:
from dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive
default_dir = get_download_dir()
vocab_file = '{}/jtvae/vocab.txt'.format(default_dir)
zip_file_path = '{}/jtvae.zip'.format(default_dir)
download(_get_dgl_url('dataset/jtvae.zip'), path=zip_file_path, overwrite=False)
extract_archive(zip_file_path, '{}/jtvae'.format(default_dir))
with open(vocab_file, 'r') as f:
self.vocab = [x.strip("\r\n ") for x in f]
else:
# Prepare a vocabulary from scratch
vocab = set()
with open(file_path, 'r') as f:
for line in f:
smiles = line.split()[0]
mol = MolTree(smiles)
for i in mol.nodes_dict:
vocab.add(mol.nodes_dict[i]['smiles'])
self.vocab = list(vocab)
self.vmap = {x: i for i, x in enumerate(self.vocab)}
self.slots = [get_slots(smiles) for smiles in self.vocab]
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./muv_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/muv.zip'
data_path = get_download_dir() + '/muv.zip'
dir_path = get_download_dir() + '/muv'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/muv.csv')
self.ids = df['mol_id'].tolist()
self.load_full = False
df = df.drop(columns=['mol_id'])
super(MUV, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
load=load,
log_every=log_every,
init_mask=True,
n_jobs=n_jobs)
self.ids = [self.ids[i] for i in self.valid_ids]
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=True,
log_every=1000,
cache_file_path='lipophilicity_dglgraph.bin'):
self._url = 'dataset/lipophilicity.zip'
data_path = get_download_dir() + '/lipophilicity.zip'
dir_path = get_download_dir() + '/lipophilicity'
download(_get_dgl_url(self._url), path=data_path)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/Lipophilicity.csv')
# ChEMBL ids
self.chembl_ids = df['CMPD_CHEMBLID'].tolist()
self.load_full = False
super(Lipophilicity, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
task_names=['exp'],
load=load,
log_every=log_every,
init_mask=False)
def __init__(self, raw_dir=None, force_reload=False, verbose=True):
url = _get_dgl_url('dataset/wn18.tgz')
super(WN18Dataset, self).__init__('wn18',
url=url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose)
def __init__(self, subset, load_binding_pocket=True, sanitize=False, calc_charges=False,
remove_hs=False, use_conformation=True,
construct_graph_and_featurize=ACNN_graph_construction_and_featurization,
zero_padding=True, num_processes=64):
self.task_names = ['-logKd/Ki']
self.n_tasks = len(self.task_names)
self._url = 'dataset/pdbbind_v2015.tar.gz'
root_dir_path = get_download_dir()
data_path = root_dir_path + '/pdbbind_v2015.tar.gz'
extracted_data_path = root_dir_path + '/pdbbind_v2015'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
extract_archive(data_path, extracted_data_path)
if subset == 'core':
index_label_file = extracted_data_path + '/v2015/INDEX_core_data.2013'
elif subset == 'refined':
index_label_file = extracted_data_path + '/v2015/INDEX_refined_data.2015'
else:
raise ValueError(
'Expect the subset_choice to be either '
'core or refined, got {}'.format(subset))
self._preprocess(extracted_data_path, index_label_file, load_binding_pocket,
sanitize, calc_charges, remove_hs, use_conformation,
construct_graph_and_featurize, zero_padding, num_processes)
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=True,
log_every=1000,
cache_file_path='AstraZeneca_chembl_solubility_graph.bin',
log_of_values=True):
self._url = 'dataset/AstraZeneca_ChEMBL_Solubility.csv'
data_path = get_download_dir() + '/AstraZeneca_ChEMBL_Solubility.csv'
download(_get_dgl_url(self._url), path=data_path)
df = pd.read_csv(data_path)
# ChEMBL ids
self.chembl_ids = df['Molecule ChEMBL ID'].tolist()
# Molecular weight
self.mol_weight = df['Molecular Weight'].tolist()
self.load_full = False
super(AstraZenecaChEMBLSolubility, self).__init__(
df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='Smiles',
cache_file_path=cache_file_path,
task_names=['Solubility'],
load=load,
log_every=log_every,
init_mask=False)
if log_of_values:
self.labels = self.labels.log()
def download_and_load_checkpoint(model_name, model, model_postfix,
local_pretrained_path='pre_trained.pth', log=True):
"""Download pretrained model checkpoint
The model will be loaded to CPU.
Parameters
----------
model_name : str
Name of the model
model : nn.Module
Instantiated model instance
model_postfix : str
Postfix for pretrained model checkpoint
local_pretrained_path : str
Local name for the downloaded model checkpoint
log : bool
Whether to print progress for model loading
Returns
-------
model : nn.Module
Pretrained model
"""
url_to_pretrained = _get_dgl_url(model_postfix)
local_pretrained_path = '_'.join([model_name, local_pretrained_path])
download(url_to_pretrained, path=local_pretrained_path, log=log)
checkpoint = torch.load(local_pretrained_path, map_location='cpu')
model.load_state_dict(checkpoint['model_state_dict'])
if log:
print('Pretrained model loaded')
return model
def __init__(self,
subset,
mol_to_graph=mol_to_bigraph,
node_featurizer=default_node_featurizer,
edge_featurizer=default_edge_featurizer,
atom_pair_featurizer=default_atom_pair_featurizer,
load=True):
assert subset in ['train', 'val', 'test'], \
'Expect subset to be "train" or "val" or "test", got {}'.format(subset)
print('Preparing {} subset of USPTO'.format(subset))
self._subset = subset
if subset == 'val':
subset = 'valid'
self._url = 'dataset/uspto.zip'
data_path = get_download_dir() + '/uspto.zip'
extracted_data_path = get_download_dir() + '/uspto'
download(_get_dgl_url(self._url), path=data_path)
extract_archive(data_path, extracted_data_path)
super(USPTO, self).__init__(
raw_file_path=extracted_data_path + '/{}.txt'.format(subset),
mol_graph_path=extracted_data_path + '/{}_mol_graphs.bin'.format(subset),
mol_to_graph=mol_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
atom_pair_featurizer=atom_pair_featurizer,
load=load)
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=True,
log_every=1000,
cache_file_path='freesolv_dglgraph.bin'):
self._url = 'dataset/FreeSolv.zip'
data_path = get_download_dir() + '/FreeSolv.zip'
dir_path = get_download_dir() + '/FreeSolv'
download(_get_dgl_url(self._url), path=data_path)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/SAMPL.csv')
# Iupac names
self.iupac_names = df['iupac'].tolist()
# Calculated hydration free energy
self.calc_energy = df['calc'].tolist()
self.load_full = False
super(FreeSolv, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
task_names=['expt'],
load=load,
log_every=log_every,
init_mask=False)
def __init__(self,
mode='dev',
mol_to_graph=mol_to_complete_graph,
node_featurizer=alchemy_nodes,
edge_featurizer=alchemy_edges,
load=True):
if mode == 'test':
raise ValueError('The test mode is not supported before '
'the Alchemy contest finishes.')
assert mode in ['dev', 'valid', 'test'], \
'Expect mode to be dev, valid or test, got {}.'.format(mode)
self.mode = mode
# Construct DGLGraphs from raw data or use the preprocessed data
self.load = load
file_dir = osp.join(get_download_dir(), 'Alchemy_data')
if load:
file_name = "{}_processed_dgl".format(mode)
else:
file_name = "{}_single_sdf".format(mode)
self.file_dir = pathlib.Path(file_dir, file_name)
self._url = 'dataset/alchemy/'
self.zip_file_path = pathlib.Path(file_dir, file_name + '.zip')
download(_get_dgl_url(self._url + file_name + '.zip'),
path=str(self.zip_file_path))
if not os.path.exists(str(self.file_dir)):
archive = zipfile.ZipFile(self.zip_file_path)
archive.extractall(file_dir)
archive.close()
self._load(mol_to_graph, node_featurizer, edge_featurizer)
def __init__(self,
smiles_to_graph=smiles_to_bigraph,
node_featurizer=None,
edge_featurizer=None,
load=False,
log_every=1000,
cache_file_path='./bbbp_dglgraph.bin',
n_jobs=1):
self._url = 'dataset/bbbp.zip'
data_path = get_download_dir() + '/bbbp.zip'
dir_path = get_download_dir() + '/bbbp'
download(_get_dgl_url(self._url), path=data_path, overwrite=False)
extract_archive(data_path, dir_path)
df = pd.read_csv(dir_path + '/BBBP.csv')
super(BBBP, self).__init__(df=df,
smiles_to_graph=smiles_to_graph,
node_featurizer=node_featurizer,
edge_featurizer=edge_featurizer,
smiles_column='smiles',
cache_file_path=cache_file_path,
task_names=['p_np'],
load=load,
log_every=log_every,
init_mask=True,
n_jobs=n_jobs)
self.load_full = False
self.names = df['name'].tolist()
self.names = [self.names[i] for i in self.valid_ids]
def load_acm_raw(remove_self_loop):
assert not remove_self_loop
url = 'dataset/ACM.mat'
data_path = get_download_dir() + '/ACM.mat'
download(_get_dgl_url(url), path=data_path)
data = sio.loadmat(data_path)
p_vs_l = data['PvsL'] # paper-field?
p_vs_a = data['PvsA'] # paper-author
p_vs_t = data['PvsT'] # paper-term, bag of words
p_vs_c = data['PvsC'] # paper-conference, labels come from that
# We assign
# (1) KDD papers as class 0 (data mining),
# (2) SIGMOD and VLDB papers as class 1 (database),
# (3) SIGCOMM and MOBICOMM papers as class 2 (communication)
conf_ids = [0, 1, 9, 10, 13]
label_ids = [0, 1, 2, 2, 1]
p_vs_c_filter = p_vs_c[:, conf_ids]
p_selected = (p_vs_c_filter.sum(1) != 0).A1.nonzero()[0]
p_vs_l = p_vs_l[p_selected]
p_vs_a = p_vs_a[p_selected]
p_vs_t = p_vs_t[p_selected]
p_vs_c = p_vs_c[p_selected]
hg = dgl.heterograph({
('paper', 'pa', 'author'): p_vs_a.nonzero(),
('author', 'ap', 'paper'): p_vs_a.transpose.nonzero(),
('paper', 'pf', 'field'): p_vs_l.nonzero(),
('field', 'fp', 'paper'): p_vs_l.transpose().nonzero()
})
features = torch.FloatTensor(p_vs_t.toarray())
pc_p, pc_c = p_vs_c.nonzero()
labels = np.zeros(len(p_selected), dtype=np.int64)
for conf_id, label_id in zip(conf_ids, label_ids):
labels[pc_p[pc_c == conf_id]] = label_id
labels = torch.LongTensor(labels)
num_classes = 3
float_mask = np.zeros(len(pc_p))
for conf_id in conf_ids:
pc_c_mask = (pc_c == conf_id)
float_mask[pc_c_mask] = np.random.permutation(
np.linspace(0, 1, pc_c_mask.sum()))
train_idx = np.where(float_mask <= 0.2)[0]
val_idx = np.where((float_mask > 0.2) & (float_mask <= 0.3))[0]
test_idx = np.where(float_mask > 0.3)[0]
num_nodes = hg.number_of_nodes('paper')
train_mask = get_binary_mask(num_nodes, train_idx)
val_mask = get_binary_mask(num_nodes, val_idx)
test_mask = get_binary_mask(num_nodes, test_idx)
return hg, features, labels, num_classes, train_idx, val_idx, test_idx, \
train_mask, val_mask, test_mask
def test_jtvae():
# Test DGLMolTree
smiles = 'CC1([C@@H](N2[C@H](S1)[C@@H](C2=O)NC(=O)CC3=CC=CC=C3)C(=O)O)C'
tree = DGLMolTree(smiles)
assert tree.treesize() == 17
tree.assemble()
assert tree._recover_node(0, tree.mol) == 'C[CH3:15]'
tree.recover()
# Test JTVAEDataset
smiles = [
'CCCCCCC1=NN2C(=N)/C(=C\c3cc(C)n(-c4ccc(C)cc4C)c3C)C(=O)N=C2S1',
'COCC[C@@H](C)C(=O)N(C)Cc1ccc(O)cc1'
]
with open('data.txt', 'w') as f:
for smi in smiles:
f.write(smi + '\n')
default_dir = get_download_dir()
vocab_file = '{}/jtnn/{}.txt'.format(default_dir, 'vocab')
zip_file_path = '{}/jtnn.zip'.format(default_dir)
download(_get_dgl_url('dataset/jtnn.zip'),
path=zip_file_path,
overwrite=False)
extract_archive(zip_file_path, '{}/jtnn'.format(default_dir))
with open(vocab_file, 'r') as f:
vocab = Vocab([x.strip("\r\n ") for x in f])
dataset = JTVAEDataset('data.txt', vocab)
assert len(dataset) == 2
assert set(dataset[0].keys()) == {
'cand_graphs', 'mol_graph', 'mol_tree', 'stereo_cand_graphs',
'stereo_cand_label', 'tree_mess_src_e', 'tree_mess_tgt_e',
'tree_mess_tgt_n', 'wid'
}
dataset.training = False
assert set(dataset[0].keys()) == {'mol_graph', 'mol_tree', 'wid'}
dataset.training = True
collate_fn = JTVAECollator(training=True)
loader = DataLoader(dataset, batch_size=2, collate_fn=collate_fn)
for _, batch_data in enumerate(loader):
assert set(batch_data.keys()) == {
'cand_batch_idx', 'cand_graph_batch', 'mol_graph_batch',
'mol_trees', 'stereo_cand_batch_idx', 'stereo_cand_graph_batch',
'stereo_cand_labels', 'stereo_cand_lengths', 'tree_mess_src_e',
'tree_mess_tgt_e', 'tree_mess_tgt_n'
}
dataset.training = False
collate_fn = JTVAECollator(training=False)
loader = DataLoader(dataset, batch_size=2, collate_fn=collate_fn)
for _, batch_data in enumerate(loader):
assert set(batch_data.keys()) == {'mol_graph_batch', 'mol_trees'}
remove_file('data.txt')
remove_file(zip_file_path)
remove_dir(default_dir + '/jtnn')
def __init__(self, smiles_to_graph=smiles_to_bigraph,
node_featurizer=None, edge_featurizer=None, load=True, log_every=1000):
self._url = 'dataset/pubchem_bioassay_aromaticity.csv'
data_path = get_download_dir() + '/pubchem_bioassay_aromaticity.csv'
download(_get_dgl_url(self._url), path=data_path)
df = pd.read_csv(data_path)
super(PubChemBioAssayAromaticity, self).__init__(
df, smiles_to_graph, node_featurizer, edge_featurizer, "cano_smiles",
"pubchem_aromaticity_dglgraph.bin", load=load, log_every=log_every)
def _download_babi_data():
download_dir = get_download_dir()
zip_file_path = os.path.join(download_dir, 'babi_data.zip')
data_url = _get_dgl_url('models/ggnn_babi_data.zip')
download(data_url, path=zip_file_path)
extract_dir = os.path.join(download_dir, 'babi_data')
if not os.path.exists(extract_dir):
extract_archive(zip_file_path, extract_dir)
def __init__(self,
data_name,
raw_dir=None,
force_reload=False,
verbose=False):
_url = _get_dgl_url(f"dataset/{data_name}.zip")
super(ExtDataset, self).__init__(name=data_name,
url=_url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose)
def load_acm_raw():
from dgl.data.utils import download, get_download_dir, _get_dgl_url
from scipy import io as sio
url = 'dataset/ACM.mat'
data_path = get_download_dir() + '/ACM.mat'
download(_get_dgl_url(url), path=data_path)
data = sio.loadmat(data_path)
p_vs_l = data['PvsL'] # paper-field?
p_vs_a = data['PvsA'] # paper-author
p_vs_t = data['PvsT'] # paper-term, bag of words
p_vs_c = data['PvsC'] # paper-conference, labels come from that
# We assign
# (1) KDD papers as class 0 (data mining),
# (2) SIGMOD and VLDB papers as class 1 (database),
# (3) SIGCOMM and MOBICOMM papers as class 2 (communication)
conf_ids = [0, 1, 9, 10, 13]
label_ids = [0, 1, 2, 2, 1]
p_vs_c_filter = p_vs_c[:, conf_ids]
p_selected = (p_vs_c_filter.sum(1) != 0).A1.nonzero()[0]
p_vs_l = p_vs_l[p_selected]
p_vs_a = p_vs_a[p_selected]
p_vs_t = p_vs_t[p_selected]
p_vs_c = p_vs_c[p_selected]
pa = dgl.bipartite(p_vs_a, 'paper', 'pa', 'author')
pl = dgl.bipartite(p_vs_l, 'paper', 'pf', 'field')
gs = [pa, pl]
hg = dgl.hetero_from_relations(gs)
features = torch.FloatTensor(p_vs_t.toarray())
pc_p, pc_c = p_vs_c.nonzero()
labels = np.zeros(len(p_selected), dtype=np.int64)
for conf_id, label_id in zip(conf_ids, label_ids):
labels[pc_p[pc_c == conf_id]] = label_id
labels = torch.LongTensor(labels)
num_classes = 3
float_mask = np.zeros(len(pc_p))
for conf_id in conf_ids:
pc_c_mask = (pc_c == conf_id)
float_mask[pc_c_mask] = np.random.permutation(
np.linspace(0, 1, pc_c_mask.sum()))
train_idx = np.where(float_mask <= 0.2)[0]
val_idx = np.where((float_mask > 0.2) & (float_mask <= 0.3))[0]
test_idx = np.where(float_mask > 0.3)[0]
hg.nodes["paper"].data["feat"] = features
return hg, labels, num_classes, train_idx, val_idx, test_idx
def readFragmentScores(name='fpscores'):
import gzip
global _fscores
fname = '{}.pkl.gz'.format(name)
download(_get_dgl_url(os.path.join('dataset', fname)), path=fname)
_fscores = cPickle.load(gzip.open(fname))
outDict = {}
for i in _fscores:
for j in range(1, len(i)):
outDict[i[j]] = float(i[0])
_fscores = outDict
def __init__(self,
name,
raw_dir=None,
random_seed=717,
train_size=0.7,
val_size=0.1):
assert name in ['gos', 'pol'], "Only supports 'gos' or 'pol'."
self.seed = random_seed
self.train_size = train_size
self.val_size = val_size
url = _get_dgl_url(self.file_urls[name])
super(GASDataset, self).__init__(name=name, url=url, raw_dir=raw_dir)
def __init__(self,
label_keys=None,
raw_dir=None,
force_reload=False,
verbose=True):
self.label_keys = label_keys
self._url = _get_dgl_url('dataset/qm9_ver2.zip')
super(QM9Dataset_v2, self).__init__(name='qm9_v2',
url=self._url,
raw_dir=raw_dir,
force_reload=force_reload,
verbose=verbose)
def __init__(self, mode='train', vocab_file=None):
self.mode = mode
self.dir = get_download_dir()
self.zip_file_path='{}/sst.zip'.format(self.dir)
self.pretrained_file = 'glove.840B.300d.txt' if mode == 'train' else ''
self.pretrained_emb = None
self.vocab_file = '{}/sst/vocab.txt'.format(self.dir) if vocab_file is None else vocab_file
download(_get_dgl_url(_urls['sst']), path=self.zip_file_path)
extract_archive(self.zip_file_path, '{}/sst'.format(self.dir))
self.trees = []
self.num_classes = 5
print('Preprocessing...')
self._load()
print('Dataset creation finished. #Trees:', len(self.trees))
def test_acnn():
remove_dir('tmp1')
remove_dir('tmp2')
url = _get_dgl_url('dgllife/example_mols.tar.gz')
local_path = 'tmp1/example_mols.tar.gz'
download(url, path=local_path)
extract_archive(local_path, 'tmp2')
pocket_mol, pocket_coords = load_molecule(
'tmp2/example_mols/example.pdb', remove_hs=True)
ligand_mol, ligand_coords = load_molecule(
'tmp2/example_mols/example.pdbqt', remove_hs=True)
remove_dir('tmp1')
remove_dir('tmp2')
if torch.cuda.is_available():
device = torch.device('cuda:0')
else:
device = torch.device('cpu')
g1 = ACNN_graph_construction_and_featurization(ligand_mol,
pocket_mol,
ligand_coords,
pocket_coords)
model = ACNN()
model.to(device)
g1.to(device)
assert model(g1).shape == torch.Size([1, 1])
bg = dgl.batch_hetero([g1, g1])
bg.to(device)
assert model(bg).shape == torch.Size([2, 1])
model = ACNN(hidden_sizes=[1, 2],
weight_init_stddevs=[1, 1],
dropouts=[0.1, 0.],
features_to_use=torch.tensor([6., 8.]),
radial=[[12.0], [0.0, 2.0], [4.0]])
model.to(device)
g1.to(device)
assert model(g1).shape == torch.Size([1, 1])
bg = dgl.batch_hetero([g1, g1])
bg.to(device)
assert model(bg).shape == torch.Size([2, 1])
def download_data(dataset, fname):
"""Download dataset if built-in support exists
Parameters
----------
dataset : str
Dataset name
fname : str
Name of dataset file
"""
if dataset not in ['ChEMBL', 'ZINC']:
# For dataset without built-in support, they should be locally processed.
return
data_path = fname
download(_get_dgl_url(os.path.join('dataset', fname)), path=data_path)
def __init__(self, data, vocab, training=True):
self.dir = get_download_dir()
self.zip_file_path = '{}/jtnn.zip'.format(self.dir)
download(_get_dgl_url('dgllife/jtnn.zip'), path=self.zip_file_path)
extract_archive(self.zip_file_path, '{}/jtnn'.format(self.dir))
print('Loading data...')
data_file = '{}/jtnn/{}.txt'.format(self.dir, data)
with open(data_file) as f:
self.data = [line.strip("\r\n ").split()[0] for line in f]
self.vocab_file = '{}/jtnn/{}.txt'.format(self.dir, vocab)
print('Loading finished.')
print('\tNum samples:', len(self.data))
print('\tVocab file:', self.vocab_file)
self.training = training
self.vocab = Vocab([x.strip("\r\n ") for x in open(self.vocab_file)])
def load_acm(remove_self_loop):
filename = 'ACM3025.pkl'
url = 'dataset/' + filename
data_path = get_download_dir() + '/' + filename
if osp.exists(data_path):
print(f'Using existing file {filename}', file=sys.stderr)
else:
download(_get_dgl_url(url), path=data_path)
with open(data_path, 'rb') as f:
data = pickle.load(f)
labels, features = torch.from_numpy(data['label'].todense()).long(), \
torch.from_numpy(data['feature'].todense()).float()
num_classes = labels.shape[1]
labels = labels.nonzero()[:, 1]
if remove_self_loop:
num_nodes = data['label'].shape[0]
data['PAP'] = sparse.csr_matrix(data['PAP'] - np.eye(num_nodes))
data['PLP'] = sparse.csr_matrix(data['PLP'] - np.eye(num_nodes))
# Adjacency matrices for meta path based neighbors
# (Mufei): I verified both of them are binary adjacency matrices with self loops
author_g = dgl.from_scipy(data['PAP'])
subject_g = dgl.from_scipy(data['PLP'])
gs = [author_g, subject_g]
train_idx = torch.from_numpy(data['train_idx']).long().squeeze(0)
val_idx = torch.from_numpy(data['val_idx']).long().squeeze(0)
test_idx = torch.from_numpy(data['test_idx']).long().squeeze(0)
num_nodes = author_g.number_of_nodes()
train_mask = get_binary_mask(num_nodes, train_idx)
val_mask = get_binary_mask(num_nodes, val_idx)
test_mask = get_binary_mask(num_nodes, test_idx)
print('dataset loaded')
pprint({
'dataset': 'ACM',
'train': train_mask.sum().item() / num_nodes,
'val': val_mask.sum().item() / num_nodes,
'test': test_mask.sum().item() / num_nodes
})
return gs, features, labels, num_classes, train_idx, val_idx, test_idx, \
train_mask, val_mask, test_mask