def get_statistics(project_id):
fp_lock = get_lock_path(project_id)
with SQLiteLock(fp_lock, blocking=True, lock_name="active"):
# get the index of the active iteration
label_history = read_label_history(project_id)
current_labels = read_current_labels(project_id,
label_history=label_history)
n_since_last_inclusion = 0
for _, inclusion in reversed(label_history):
if inclusion == 1:
break
n_since_last_inclusion += 1
n_included = len(np.where(current_labels == 1)[0])
n_excluded = len(np.where(current_labels == 0)[0])
n_papers = len(current_labels)
stats = {
"n_included": n_included,
"n_excluded": n_excluded,
"n_since_last_inclusion": n_since_last_inclusion,
"n_papers": n_papers,
"n_pool": n_papers - n_included - n_excluded
}
return stats
def export_to_string(project_id, export_type="csv"):
fp_lock = get_lock_path(project_id)
as_data = read_data(project_id)
with SQLiteLock(fp_lock, blocking=True, lock_name="active"):
proba = read_proba(project_id)
if proba is None:
proba = np.flip(np.arange(len(as_data)))
else:
proba = np.array(proba)
labels = read_current_labels(project_id, as_data=as_data)
pool_idx = np.where(labels == LABEL_NA)[0]
one_idx = np.where(labels == 1)[0]
zero_idx = np.where(labels == 0)[0]
proba_order = np.argsort(-proba[pool_idx])
ranking = np.concatenate((one_idx, pool_idx[proba_order], zero_idx),
axis=None)
if export_type == "csv":
return as_data.to_csv(fp=None, labels=labels, ranking=ranking)
if export_type == "excel":
get_tmp_path(project_id).mkdir(exist_ok=True)
fp_tmp_export = Path(get_tmp_path(project_id), "export_result.xlsx")
return as_data.to_excel(fp=fp_tmp_export,
labels=labels,
ranking=ranking)
else:
raise ValueError("This export type isn't implemented.")
def export_to_string(project_id):
fp_lock = get_lock_path(project_id)
as_data = read_data(project_id)
with SQLiteLock(fp_lock, blocking=True, lock_name="active"):
proba = read_proba(project_id)
if proba is None:
proba = np.flip(np.arange(len(as_data)))
else:
proba = np.array(proba)
labels = read_current_labels(project_id, as_data=as_data)
pool_idx = np.where(labels == LABEL_NA)[0]
one_idx = np.where(labels == 1)[0]
zero_idx = np.where(labels == 0)[0]
proba_order = np.argsort(-proba[pool_idx])
ranking = np.concatenate((one_idx, pool_idx[proba_order], zero_idx),
axis=None)
return as_data.to_csv(fp=None, labels=labels, ranking=ranking)