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 _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 target_4_dataset_save(self,dataset,file):
compound=dataset.ids.tolist()
target=dataset.get_task_names()
print(target)
w=dataset.w
print('w.shape')
print(w.shape)
compuond_4_target=[]
target_4=['P21728','P14416','P08908','P28223']
target_4=sorted(target_4,key=lambda x:target.index(x))
target_4_index=[target.index(i) for i in target_4]
print('target_4')
print(target_4_index)
for i in range(len(compound)):
z=0
for j in target_4_index:
if w[i,j]>0:
z=z+1
if z>0:
compuond_4_target.append(i)
compound_shard=[]
dataset1=dataset.select(compuond_4_target)
print(compuond_4_target)
cpd=compuond_4_target
metadata_rows=[]
shard_generator=self.shard_generator(cpd,target_4_index,dataset1)
for shard_num, (X, y, w, ids) in enumerate(shard_generator):
basename = "shard-%d" % shard_num
metadata_rows.append(
DiskDataset.write_data_to_disk(file, basename,target_4 , X, y, w,
ids))
metadata_df = DiskDataset._construct_metadata(metadata_rows)
self.save_metadata(target_4, metadata_df, file)
time2 = time.time()
