def move_label_from_labeled_to_pool(project_id, paper_i):
# load the papers from the pool
pool_list = read_pool(project_id)
# Add the paper to the reviewed papers.
labeled_list = read_label_history(project_id)
labeled_list_new = []
for item_id, item_label in labeled_list:
item_id = int(item_id)
item_label = int(item_label)
paper_i = int(paper_i)
if paper_i == item_id:
pool_list.append(item_id)
else:
labeled_list_new.append([item_id, item_label])
# write the papers to the label dataset
write_pool(project_id, pool_list)
# load the papers from the pool
write_label_history(project_id, labeled_list_new)
def add_dataset_to_project(project_id, file_name):
"""Add file path to the project file.
Add file to data subfolder and fill the pool of iteration 0.
"""
project_file_path = get_project_file_path(project_id)
fp_lock = get_lock_path(project_id)
with SQLiteLock(fp_lock, blocking=True, lock_name="active"):
# open the projects file
with open(project_file_path, "r") as f_read:
project_dict = json.load(f_read)
# add path to dict (overwrite if already exists)
project_dict["dataset_path"] = file_name
with open(project_file_path, "w") as f_write:
json.dump(project_dict, f_write)
# fill the pool of the first iteration
pool_indices = read_data(project_id).record_ids
np.random.shuffle(pool_indices)
write_pool(project_id, pool_indices.tolist())
# make a empty qeue for the items to label
write_label_history(project_id, [])
def add_dataset_to_project(project_id, file_name):
"""Add file path to the project file.
Add file to data subfolder and fill the pool of iteration 0.
"""
project_file_path = get_project_file_path(project_id)
# clean temp project files
clean_project_tmp_files(project_id)
with SQLiteLock(
get_lock_path(project_id),
blocking=True,
lock_name="active",
project_id=project_id
):
# open the projects file
with open(project_file_path, "r") as f_read:
project_dict = json.load(f_read)
# add path to dict (overwrite if already exists)
project_dict["dataset_path"] = file_name
with open(project_file_path, "w") as f_write:
json.dump(project_dict, f_write)
# fill the pool of the first iteration
as_data = read_data(project_id)
if as_data.labels is not None:
unlabeled = np.where(as_data.labels == LABEL_NA)[0]
pool_indices = as_data.record_ids[unlabeled]
labeled_indices = np.where(as_data.labels != LABEL_NA)[0]
label_indices = list(zip(
as_data.record_ids[labeled_indices].tolist(),
as_data.labels[labeled_indices].tolist()
))
else:
pool_indices = as_data.record_ids
label_indices = []
np.random.shuffle(pool_indices)
write_pool(project_id, pool_indices.tolist())
# make a empty qeue for the items to label
write_label_history(project_id, label_indices)
def move_label_from_pool_to_labeled(project_id, paper_i, label):
# load the papers from the pool
pool_idx = read_pool(project_id)
# Remove the paper from the pool.
try:
pool_idx.remove(int(paper_i))
except (IndexError, ValueError):
return
write_pool(project_id, pool_idx)
# Add the paper to the reviewed papers.
labeled = read_label_history(project_id)
labeled.append([int(paper_i), int(label)])
write_label_history(project_id, labeled)
def add_dataset_to_project(project_id, file_name):
"""Add file path to the project file.
Add file to data subfolder and fill the pool of iteration 0.
"""
project_file_path = get_project_file_path(project_id)
fp_lock = get_lock_path(project_id)
with SQLiteLock(fp_lock, blocking=True, lock_name="active"):
# open the projects file
with open(project_file_path, "r") as f_read:
project_dict = json.load(f_read)
# add path to dict (overwrite if already exists)
project_dict["dataset_path"] = file_name
with open(project_file_path, "w") as f_write:
json.dump(project_dict, f_write)
# fill the pool of the first iteration
as_data = read_data(project_id)
if as_data.labels is not None:
unlabeled = np.where(as_data.labels == LABEL_NA)[0]
pool_indices = as_data.record_ids[unlabeled]
label_indices_included = \
[[int(x), 1] for x in np.where(as_data.labels == 1)[0]]
label_indices_excluded = \
[[int(x), 0] for x in np.where(as_data.labels == 0)[0]]
label_indices = label_indices_included + label_indices_excluded
else:
pool_indices = as_data.record_ids
label_indices = []
np.random.shuffle(pool_indices)
write_pool(project_id, pool_indices.tolist())
# make a empty qeue for the items to label
write_label_history(project_id, label_indices)
