def load_tox21(featurizer='ECFP', split='index'):
"""Load Tox21 datasets. Does not do train/test split"""
# Featurize Tox21 dataset
print("About to featurize Tox21 dataset.")
current_dir = os.path.dirname(os.path.realpath(__file__))
dataset_file = os.path.join(current_dir, "../../datasets/tox21.csv.gz")
data_dir = deepchem.utils.get_data_dir()
tox21_tasks = [
'NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER', 'NR-ER-LBD',
'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5', 'SR-HSE', 'SR-MMP', 'SR-p53'
]
dataset_dir = os.path.join(data_dir, "tox21", featurizer, split)
train, valid, test = os.path.join(dataset_dir, 'train'), os.path.join(
dataset_dir, 'valid'), os.path.join(dataset_dir, 'test')
if os.path.isdir(dataset_dir):
train, valid, test = DiskDataset(data_dir=train), DiskDataset(
data_dir=valid), DiskDataset(data_dir=test)
transformers = [
dc.trans.BalancingTransformer(transform_w=True, dataset=train)
]
return tox21_tasks, (train, valid, test), transformers
if featurizer == 'ECFP':
featurizer_func = dc.feat.CircularFingerprint(size=1024)
elif featurizer == 'GraphConv':
featurizer_func = dc.feat.ConvMolFeaturizer()
elif featurizer == 'AdjMatrix':
featurizer_func = dc.feat.AdjacencyFingerprint(num_atoms_feature=True)
loader = dc.data.CSVLoader(tasks=tox21_tasks,
smiles_field="smiles",
featurizer=featurizer_func)
dataset = loader.featurize(dataset_file, shard_size=8192)
# Initialize transformers
transformers = [
dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)
]
print("About to transform data")
for transformer in transformers:
dataset = transformer.transform(dataset)
splitters = {
'index': dc.splits.IndexSplitter(),
'random': dc.splits.RandomSplitter(),
'scaffold': dc.splits.ScaffoldSplitter(),
'butina': dc.splits.ButinaSplitter()
}
splitter = splitters[split]
train, valid, test = splitter.train_valid_test_split(dataset,
train_dir=train,
valid_dir=valid,
test_dir=test)
return tox21_tasks, (train, valid, test), transformers
def _to_singletask(dataset, task_dirs):
"""Transforms a multitask dataset to a collection of singletask datasets."""
tasks = dataset.get_task_names()
assert len(tasks) == len(task_dirs)
log("Splitting multitask dataset into singletask datasets", dataset.verbosity)
task_metadata_rows = {task: [] for task in tasks}
for shard_num, (X, y, w, ids) in enumerate(dataset.itershards()):
log("Processing shard %d" % shard_num, dataset.verbosity)
basename = "dataset-%d" % shard_num
for task_num, task in enumerate(tasks):
log("\tTask %s" % task, dataset.verbosity)
w_task = w[:, task_num]
y_task = y[:, task_num]
# Extract those datapoints which are present for this task
X_nonzero = X[w_task != 0]
num_datapoints = X_nonzero.shape[0]
y_nonzero = np.reshape(y_task[w_task != 0], (num_datapoints, 1))
w_nonzero = np.reshape(w_task[w_task != 0], (num_datapoints, 1))
ids_nonzero = ids[w_task != 0]
if X_nonzero.size > 0:
task_metadata_rows[task].append(
DiskDataset.write_data_to_disk(
task_dirs[task_num], basename, [task],
X_nonzero, y_nonzero, w_nonzero, ids_nonzero))
task_datasets = [
DiskDataset(data_dir=task_dirs[task_num],
metadata_rows=task_metadata_rows[task],
verbosity=dataset.verbosity)
for (task_num, task) in enumerate(tasks)]
return task_datasets
def load_dataset(
self, name: str, reload: bool
) -> Tuple[List[str], Tuple[Dataset, ...], List[dc.trans.Transformer]]:
"""Load the dataset.
Parameters
----------
name: str
the name of the dataset, used to identify the directory on disk
reload: bool
if True, the first call for a particular featurizer and splitter will cache
the datasets to disk, and subsequent calls will reload the cached datasets.
"""
# Build the path to the dataset on disk.
featurizer_name = str(self.featurizer)
splitter_name = 'None' if self.splitter is None else str(self.splitter)
save_folder = os.path.join(self.save_dir, name + "-featurized",
featurizer_name, splitter_name)
if len(self.transformers) > 0:
transformer_name = '_'.join(
t.get_directory_name() for t in self.transformers)
save_folder = os.path.join(save_folder, transformer_name)
# Try to reload cached datasets.
if reload:
if self.splitter is None:
if os.path.exists(save_folder):
transformers = dc.utils.data_utils.load_transformers(save_folder)
return self.tasks, (DiskDataset(save_folder),), transformers
else:
loaded, all_dataset, transformers = dc.utils.data_utils.load_dataset_from_disk(
save_folder)
if all_dataset is not None:
return self.tasks, all_dataset, transformers
# Create the dataset
logger.info("About to featurize %s dataset." % name)
dataset = self.create_dataset()
# Split and transform the dataset.
if self.splitter is None:
transformer_dataset: Dataset = dataset
else:
logger.info("About to split dataset with {} splitter.".format(
self.splitter.__class__.__name__))
train, valid, test = self.splitter.train_valid_test_split(dataset)
transformer_dataset = train
transformers = [
t.create_transformer(transformer_dataset) for t in self.transformers
]
logger.info("About to transform data.")
if self.splitter is None:
for transformer in transformers:
dataset = transformer.transform(dataset)
if reload and isinstance(dataset, DiskDataset):
dataset.move(save_folder)
dc.utils.data_utils.save_transformers(save_folder, transformers)
return self.tasks, (dataset,), transformers
for transformer in transformers:
train = transformer.transform(train)
valid = transformer.transform(valid)
test = transformer.transform(test)
if reload and isinstance(train, DiskDataset) and isinstance(
valid, DiskDataset) and isinstance(test, DiskDataset):
dc.utils.data_utils.save_dataset_to_disk(save_folder, train, valid, test,
transformers)
return self.tasks, (train, valid, test), transformers
def load_gpcr(dataset_file,
featurizer='ECFP',
transformers=True,
reload=True,
sep='OnePositiveSplit',
K=5):
# data_dir=os.path.dirname(dataset_file)
save_dir = os.path.join(
os.path.dirname(dataset_file),
'.'.join(os.path.basename(dataset_file).split('.')[:-1]), "ecfp",
"split")
train, valid, test = os.path.join(save_dir, 'train'), os.path.join(
save_dir, 'valid'), os.path.join(save_dir, 'test')
fopen = open(dataset_file, "r")
ss = fopen.readlines()
m = ss[0].strip('\n').split(',')
m.remove('SMILES')
if os.path.isdir(save_dir):
if reload:
dataset, train_dataset, valid_dataset, test_dataset = DiskDataset(
data_dir=save_dir), DiskDataset(data_dir=train), DiskDataset(
data_dir=valid), DiskDataset(data_dir=test)
transformers = [
deepchem.trans.NormalizationTransformer(transform_w=True,
dataset=train_dataset)
]
all_dataset = (dataset, train_dataset, valid_dataset, test_dataset)
return m, all_dataset, transformers
if featurizer == 'ECFP':
featurizer = deepchem.feat.CircularFingerprint(size=1024)
elif featurizer == 'GraphConv':
featurizer = deepchem.feat.ConvMolFeaturizer()
elif featurizer == 'Weave':
featurizer = deepchem.feat.WeaveFeaturizer()
elif featurizer == 'Raw':
featurizer = deepchem.feat.RawFeaturizer()
elif featurizer == 'AdjacencyConv':
featurizer = deepchem.feat.AdjacencyFingerprint(max_n_atoms=150,
max_valence=6)
elif featurizer == 'SelfDefine':
featurizer = deepchem.feat.UserDefinedFeaturizer(feature_field)
loader = deepchem.data.CSVLoader(tasks=m,
smiles_field="SMILES",
featurizer=featurizer)
dataset = loader.featurize(dataset_file,
data_dir=save_dir,
shard_size=8192)
# dataset = loader.featurize(dataset_file, shard_size=8192)
# Initialize transformers
if transformers:
transformers = [
deepchem.trans.NormalizationTransformer(transform_w=True,
dataset=dataset)
]
for transformer in transformers:
dataset = transformer.transform(dataset)
splitters = {
'index': deepchem.splits.IndexSplitter(),
'random': deepchem.splits.RandomSplitter(),
'random_stratified': deepchem.splits.RandomStratifiedSplitter(),
'scaffold': deepchem.splits.ScaffoldSplitter(),
'butina': deepchem.splits.ButinaSplitter(),
'task': deepchem.splits.TaskSplitter(),
'Harmonious_positive': Harmonious_positive(),
'OnePositiveSplit': OnePositiveSplit()
}
splitter = splitters[sep]
if sep == 'task':
fold_datasets = splitter.k_fold_split(dataset, K)
all_dataset = fold_datasets
elif sep == 'Harmonious_positive':
train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
dataset)
train_dataset = DiskDataset.from_numpy(train_dataset.X,
train_dataset.y,
train_dataset.w,
train_dataset.ids,
dataset.tasks,
data_dir=train)
valid_dataset = DiskDataset.from_numpy(valid_dataset.X,
valid_dataset.y,
valid_dataset.w,
valid_dataset.ids,
dataset.tasks,
data_dir=valid)
test_dataset = DiskDataset.from_numpy(test_dataset.X,
test_dataset.y,
test_dataset.w,
test_dataset.ids,
dataset.tasks,
data_dir=test)
all_dataset = (dataset, train_dataset, valid_dataset, test_dataset)
elif sep == 'Harmonious_positive' and K:
# train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
# dataset,
# frac_train=frac_train,
# frac_valid=0,
# frac_test=1- frac_train,
# )
# train_dataset = DiskDataset.from_numpy(train_dataset.X,train_dataset.y,train_dataset.w,train_dataset.ids,
# dataset.tasks,data_dir=train)
# train_dataset.reshard(8192)
# test_dataset = DiskDataset.from_numpy(test_dataset.X,test_dataset.y,test_dataset.w,test_dataset.ids,
# dataset.tasks,data_dir=test)
# test_dataset.reshard(8192)
# fold_dataset = splitter.k_fold_split(
# train_dataset, K, directories=[os.path.join(valid,str(i)) for i in range(K)],verbose=True)
fold_dataset = splitter.k_fold_split(
dataset,
K,
directories=[os.path.join(valid, str(i)) for i in range(K)],
verbose=True)
folds = []
for i in range(K):
print('merge fold dataset {}...'.format(i))
train_fold = DiskDataset.merge(
[fold_dataset[j] for j in range(K) if j != i],
merge_dir=os.path.join(valid, str(i), 'train_fold'))
test_fold = DiskDataset.merge([fold_dataset[i]],
merge_dir=os.path.join(
valid, str(i), 'valid_fold'))
folds.append([train_fold, test_fold])
all_dataset = (dataset, [], folds, [])
else:
train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(
dataset,
train_dir=train,
valid_dir=valid,
test_dir=test,
frac_train=frac_train,
frac_valid=frac_valid,
frac_test=frac_test)
all_dataset = (dataset, train_dataset, valid_dataset, test_dataset)
# else:
# train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(dataset,train_dir=train, valid_dir=valid, test_dir=test)
# all_dataset = (dataset,train_dataset, valid_dataset, test_dataset)
# if reload:
# deepchem.utils.save.save_dataset_to_disk(save_dir, train, valid, test,transformers)
return m, all_dataset, transformers
def load_tf(samp_num=0,
reload=True,
split='random',
frac_train_and_valid=0.9,
data_time=10,
data_num=1,
data_dir='/home/hdd2/lifei/sam/script/tmm/data'):
tf_tasks = ['values']
dataset_file = (data_dir + "/fingerprint_" + str(samp_num) + '.csv')
save_dir = os.path.join(
os.path.dirname(dataset_file),
'.'.join(os.path.basename(dataset_file).split('.')[:-1]))
dataset_dir, test_dir = os.path.join(save_dir, 'dataset'), os.path.join(
save_dir, 'test')
if os.path.isdir(save_dir):
if reload:
dataset, train_dataset, valid_dataset, test_dataset = DiskDataset(
data_dir=dataset_dir), DiskDataset(
data_dir=os.path.join(save_dir, (
'train_vaild_' +
str(data_num)), 'train')), DiskDataset(
data_dir=os.path.join(save_dir, (
'train_vaild_' +
str(data_num)), 'valid')), DiskDataset(
data_dir=test_dir)
all_dataset = (dataset, train_dataset, valid_dataset, test_dataset)
return all_dataset
else:
print("About to featurize TF dataset.")
featurizer = dc.feat.UserDefinedFeaturizer(tf_descriptors)
loader = dc.data.UserCSVLoader(tasks=tf_tasks,
id_field="compounds",
featurizer=featurizer)
if not os.path.exists(dataset_dir):
os.makedirs(dataset_dir)
dataset = loader.featurize(dataset_file,
data_dir=dataset_dir,
shard_size=8192)
splitters = {
'index': dc.splits.IndexSplitter(),
'random': dc.splits.RandomSplitter(),
'scaffold': dc.splits.ScaffoldSplitter()
}
splitter = splitters[split]
if not os.path.exists(test_dir):
os.makedirs(test_dir)
train_valid, test_dataset = splitter.train_test_split(
dataset, test_dir=test_dir, frac_train=frac_train_and_valid)
test_df = pd.DataFrame(test_dataset.ids)
test_df.to_csv(os.path.join(test_dir, 'test.csv'))
for i in range(data_time):
train_valid_dir = os.path.join(save_dir,
('train_vaild_' + str(i + 1)))
train_dir, valid_dir = os.path.join(train_valid_dir,
'train'), os.path.join(
train_valid_dir, 'valid')
for i in (train_dir, valid_dir):
if not os.path.exists(i):
os.makedirs(i)
train_dataset_t, vaild_dataset_t = splitter.train_test_split(
train_valid,
train_dir=train_dir,
test_dir=valid_dir,
frac_train=8.0 / 9)
train_df, vaild_df = pd.DataFrame(
train_dataset_t.ids), pd.DataFrame(vaild_dataset_t.ids)
train_df.to_csv(train_dir + '/train.csv')
vaild_df.to_csv(valid_dir + '/valid.csv')
train_dataset, valid_dataset = DiskDataset(
data_dir=os.path.join(save_dir, (
'train_vaild_' + str(data_num)), 'train')), DiskDataset(
data_dir=os.path.join(save_dir, ('train_vaild_' +
str(data_num)), 'valid'))
all_dataset = (dataset, train_dataset, valid_dataset, test_dataset)
return all_dataset
def featurize(self, input_files, data_dir=None, shard_size=8192,
num_shards_per_batch=24, worker_pool=None,
logging=True, debug=False):
"""Featurize provided files and write to specified location."""
############################################################## TIMING
time1 = time.time()
############################################################## TIMING
log("Loading raw samples now.", self.verbosity)
log("shard_size: %d" % shard_size, self.verbosity)
log("num_shards_per_batch: %d" % num_shards_per_batch, self.verbosity)
# Allow users to specify a single file for featurization
if not isinstance(input_files, list):
input_files = [input_files]
if data_dir is not None:
if not os.path.exists(data_dir):
os.makedirs(data_dir)
else:
data_dir = tempfile.mkdtemp()
# Construct partial function to write datasets.
if not len(input_files):
return None
input_type = get_input_type(input_files[0])
if logging:
mp.log_to_stderr()
if worker_pool is None:
if logging:
worker_pool = LoggingPool(processes=1)
else:
worker_pool = mp.Pool(processes=1)
log("Spawning workers now.", self.verbosity)
metadata_rows = []
def wrap_with_shard_metadata(iterator):
for item in iterator:
yield ((self, shard_size, input_type, data_dir), item)
data_iterator = wrap_with_shard_metadata(
enumerate(load_data(input_files, shard_size, self.verbosity)))
# Turns out python map is terrible and exhausts the generator as given.
# Solution seems to be to to manually pull out N elements from iterator,
# then to map on only those N elements. BLECH. Python should do a better
# job here.
num_batches = 0
############################################################## TIMING
time2 = time.time()
log("TIMING: pre-map featurization took %0.3f s" % (time2-time1))
############################################################## TIMING
while True:
log("About to start processing next batch of shards", self.verbosity)
############################################################## TIMING
time1 = time.time()
############################################################## TIMING
iterator = itertools.islice(data_iterator, num_shards_per_batch)
if not debug:
batch_metadata = worker_pool.map(
featurize_map_function, iterator)
else:
batch_metadata = []
for elt in iterator:
batch_metadata.append(featurize_map_function(elt))
############################################################## TIMING
time2 = time.time()
log("TIMING: map call on batch took %0.3f s" % (time2-time1),
self.verbosity)
############################################################## TIMING
if batch_metadata:
metadata_rows.extend([elt for elt in batch_metadata if elt is not None])
num_batches += 1
log("Featurized %d datapoints\n"
% (shard_size * num_shards_per_batch * num_batches), self.verbosity)
else:
break
############################################################## TIMING
time1 = time.time()
############################################################## TIMING
# TODO(rbharath): This whole bit with metadata_rows is an awkward way of
# creating a Dataset. Is there a more elegant solutions?
dataset = DiskDataset(data_dir=data_dir,
metadata_rows=metadata_rows,
reload=True, verbosity=self.verbosity)
############################################################## TIMING
time2 = time.time()
print("TIMING: dataset construction took %0.3f s" % (time2-time1),
self.verbosity)
############################################################## TIMING
return dataset
