def send_model_task(project_pk):
"""Trains, Saves, Predicts, Fills Queue."""
from core.models import Project, TrainingSet
from core.utils.utils_model import predict_data, train_and_save_model
from core.utils.utils_queue import fill_queue, find_queue_length
project = Project.objects.get(pk=project_pk)
queue = project.queue_set.get(type="normal")
irr_queue = project.queue_set.get(type="irr")
al_method = project.learning_method
batch_size = project.batch_size
model = train_and_save_model(project)
if al_method != "random":
predict_data(project, model)
TrainingSet.objects.create(
project=project,
set_number=project.get_current_training_set().set_number + 1)
# Determine if queue size has changed (num_coders changed) and re-fill queue
num_coders = len(project.projectpermissions_set.all()) + 1
q_length = find_queue_length(batch_size, num_coders)
if q_length != queue.length:
queue.length = q_length
queue.save()
fill_queue(
queue,
irr_queue=irr_queue,
orderby=al_method,
irr_percent=project.percentage_irr,
batch_size=batch_size,
)
def test_predict_data(test_project_with_trained_model, tmpdir):
project = test_project_with_trained_model
predictions = predict_data(project, project.model_set.get())
# Number of unlabeled data * number of labels. Each data gets a prediction for each label.
expected_predction_count = project.data_set.filter(
datalabel__isnull=True).count() * project.labels.count()
assert len(predictions) == expected_predction_count
for prediction in predictions:
assert isinstance(prediction, DataPrediction)
assert_obj_exists(
DataPrediction, {
'data': prediction.data,
'model': prediction.model,
'label': prediction.label,
'predicted_probability': prediction.predicted_probability
})
def test_project_predicted_data(test_project_with_trained_model, tmpdir):
project = test_project_with_trained_model
predict_data(project, project.model_set.get())
return test_project_with_trained_model