Rewrite the task assignment flow

With the demo in place, we're rewriting the
task assignment flow such that users only
get a task assigned when:

1. They finish the demo
2. They finish a task
3. They login again.

This way we can better balance the datasets
and we won't have datasets that don't get
enough annotations because some users didn't
finish tasks they were assigned.

1 files changed, 40 insertions, 84 deletions

diff --git a/app/utils/tasks.py b/app/utils/tasks.py
index a5398eb..4297420 100644
--- a/app/utils/tasks.py
+++ b/app/utils/tasks.py
@@ -4,100 +4,56 @@
 
 """
 
-import random
 
 from flask import current_app
 
 from app.models import User, Dataset, Task
 
 
-def generate_auto_assign_tasks(max_per_user, num_per_dataset):
-    """Automatically generate random tasks
-
-    This function generates random tasks for the users based on the desired 
-    number of tasks per dataset and the maximum number of tasks per user. The 
-    return value is a tuple (Task, error) where Task is None if an error 
-    occurred.
+def generate_user_task(user):
     """
+    Generate new task for a given user.
+
+    This function assigns tasks to a given user and ensures that:
+
+        1) datasets that are nearly annotated with the desired number of 
+        datasets get priority
+        2) users never are given more tasks than max_per_user
+        3) users never get the same dataset twice
 
-    # create a dictionary of user/num available tasks
-    available_users = {}
-    for user in User.query.all():
-        user_tasks = Task.query.filter_by(annotator_id=user.id).all()
-        if len(user_tasks) < max_per_user:
-            available_users[user] = max_per_user - len(user_tasks)
-
-    if not available_users:
-        error = (
-            "All users already have at least %i tasks assigned to them."
-            % max_per_user
-        )
-        yield (None, error)
-
-    # create a dictionary of dataset/num tasks desired
-    datasets_tbd = {}
-    for dataset in Dataset.query.all():
-        dataset_tasks = Task.query.filter_by(dataset_id=dataset.id).all()
-        if len(dataset_tasks) < num_per_dataset:
-            datasets_tbd[dataset] = num_per_dataset - len(dataset_tasks)
-
-    if not datasets_tbd:
-        error = (
-            "All datasets already have at least the desired number (%i) of tasks."
-            % num_per_dataset
-        )
-        yield (None, error)
-
-    # shuffle the dataset list
-    datasets = list(datasets_tbd.keys())
-    random.shuffle(datasets)
-    for dataset in datasets:
-        available = [u for u, v in available_users.items() if v > 0]
-        tbd = min(len(available), datasets_tbd[dataset])
-
-        # select a random set of users
-        selected_users = random.sample(available, tbd)
-        for user in selected_users:
-            task = Task(annotator_id=user.id, dataset_id=dataset.id)
-            yield (task, None)
-            available_users[user] -= 1
-            datasets_tbd[dataset] -= 1
-
-    if any((datasets_tbd[d] > 0 for d in datasets)):
-        yield (
-            None,
-            "Insufficient users available for the desired number of tasks per dataset.",
-        )
-
-
-def create_initial_user_tasks(user, max_per_user=None, num_per_dataset=None):
-    """Generate initial tasks for a given user
     """
-    if max_per_user is None:
-        max_per_user = current_app.config["TASKS_MAX_PER_USER"]
-    if num_per_dataset is None:
-        num_per_dataset = current_app.config["TASKS_NUM_PER_DATASET"]
+    max_per_user = current_app.config["TASKS_MAX_PER_USER"]
+    num_per_dataset = current_app.config["TASKS_NUM_PER_DATASET"]
 
     user_tasks = Task.query.filter_by(annotator_id=user.id).all()
-    if len(user_tasks) >= max_per_user:
-        yield None
-    available_user = max_per_user - len(user_tasks)
+    user_tasks = [t for t in user_tasks if not t.dataset.is_demo]
+    n_user_tasks = len(user_tasks)
+    if n_user_tasks >= max_per_user:
+        return None
 
-    datasets_tbd = {}
-    for dataset in Dataset.query.all():
+    potential_datasets = []
+    for dataset in Dataset.query.filter_by(is_demo=False).all():
         dataset_tasks = Task.query.filter_by(dataset_id=dataset.id).all()
-        if len(dataset_tasks) < num_per_dataset:
-            datasets_tbd[dataset] = num_per_dataset - len(dataset_tasks)
-    if not datasets_tbd:
-        yield None
-
-    # shuffle the dataset list
-    datasets = list(datasets_tbd.keys())
-    random.shuffle(datasets)
-    for dataset in datasets:
-        task = Task(annotator_id=user.id, dataset_id=dataset.id)
-        yield task
-        available_user -= 1
-        datasets_tbd[dataset] -= 1
-        if available_user == 0:
-            break
+
+        # 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(
+            dataset_id=dataset.id, annotator_id=user.id
+        ).first()
+        if not task is None:
+            continue
+        potential_datasets.append((n_needed, dataset))
+
+    if len(potential_datasets) == 0:
+        return None
+
+    # sort datasets so that the ones who need the least are at the front.
+    potential_datasets.sort()
+
+    _, dataset = potential_datasets[0]
+    task = Task(annotator_id=user.id, dataset_id=dataset.id)
+    return task