diff options
Diffstat (limited to 'app/utils/tasks.py')
| -rw-r--r-- | app/utils/tasks.py | 34 |
1 files changed, 28 insertions, 6 deletions
diff --git a/app/utils/tasks.py b/app/utils/tasks.py index 61155de..b0f5c27 100644 --- a/app/utils/tasks.py +++ b/app/utils/tasks.py @@ -4,6 +4,7 @@ """ +import random from flask import current_app @@ -38,14 +39,13 @@ def generate_user_task(user): if n_user_tasks >= max_per_user: return None + # collect datasets that can be potentially assigned to the user potential_datasets = [] for dataset in Dataset.query.filter_by(is_demo=False).all(): dataset_tasks = Task.query.filter_by(dataset_id=dataset.id).all() # check that this dataset needs more annotations n_needed = num_per_dataset - len(dataset_tasks) - if n_needed <= 0: - continue # check that this dataset is not already assigned to the user task = Task.query.filter_by( @@ -55,13 +55,35 @@ def generate_user_task(user): continue potential_datasets.append((n_needed, dataset)) - # don't assign a dataset if there are no more datasets to annotate + # don't assign a dataset if there are no more datasets to annotate (user + # has done all) if len(potential_datasets) == 0: return None - # sort datasets so that the ones who need the least are at the front. - potential_datasets.sort(key=lambda x: x[0]) + # First try assigning a random dataset that still needs annotations + dataset = None + need_annotations = [d for n, d in potential_datasets if n > 0] + + # Weights are set to prioritize datasets that need fewer annotations to + # reach our goal (num_per_dataset), with a small chance of selecting + # another dataset. + weights = [ + (num_per_dataset - n + 0.01) for n, d in potential_datasets if n > 0 + ] + if need_annotations: + dataset = random.choices(need_annotations, weights=weights)[0] + else: + # if there are no such datasets, then this user is requesting + # additional annotations after all datasets have our desired coverage + # of num_per_dataset. Assign a random dataset that has the least excess + # annotations (thus causing even distribution). + max_nonpos = max((n for n, d in potential_datasets if n <= 0)) + extra = [d for n, d in potential_datasets if n == max_nonpos] + if extra: + dataset = random.choice(extra) + + if dataset is None: + return None - _, dataset = potential_datasets[0] task = Task(annotator_id=user.id, dataset_id=dataset.id) return task |