def submit_ranking():
# get the username and their dataset
username = request.form['username']
user_dataset = utils_users.get_gp_dataset(
username, 'ranking', num_objectives=specs_jobs.NUM_OBJECTIVES)
# get the ranking the user submitted
ranking = np.fromstring(request.form['rankingResult'],
sep=',',
dtype=np.int)
# save it
utils_users.save_ranking(username, ranking)
# get the actual jobs (ranking returned the indiced in the dataset)
jobs_ranked = user_dataset.datapoints[ranking]
# add the ranking to the dataset
user_dataset.add_ranked_preferences(jobs_ranked)
# save
utils_users.update_gp_dataset(username, user_dataset, 'ranking')
# get the start time for this user
start_time = utils_users.get_experiment_start_time(username, 'ranking')
if time.time() - start_time < specs_jobs.TIME_EXPERIMENT_SEC:
return redirect(url_for('.start', username=username))
else:
utils_users.update_experiment_status(username=username,
query_type='ranking')
return redirect('start_experiment/{}'.format(username))
def submit_clustering_jobs():
# get the username and their dataset
username = request.form['username']
user_dataset = utils_users.get_gp_dataset(username, 'clustering', num_objectives=specs_jobs.NUM_OBJECTIVES)
# get the clusters the user submitted
top_job = [int(request.form['top-cluster'])]
good_jobs = np.fromstring(request.form['good-cluster'], sep=',', dtype=np.int)
bad_jobs = np.fromstring(request.form['bad-cluster'], sep=',', dtype=np.int)
clustering = [top_job, good_jobs, bad_jobs]
# save it
utils_users.save_clustering(username, clustering)
# get the actual jobs (ranking returned the indiced in the dataset)
top_job = user_dataset.datapoints[top_job]
good_jobs = user_dataset.datapoints[good_jobs]
bad_jobs = user_dataset.datapoints[bad_jobs]
clustering = [top_job, good_jobs, bad_jobs]
# add the ranking to the dataset
user_dataset.add_clustered_preferences(clustering)
# save
utils_users.update_gp_dataset(username, user_dataset, 'clustering')
# get the start time for this user
start_time = utils_users.get_experiment_start_time(username, 'clustering')
if time.time()-start_time < specs_jobs.TIME_EXPERIMENT_SEC:
return redirect(url_for('.start', username=username))
else:
utils_users.update_experiment_status(username=username, query_type='clustering')
return redirect('start_experiment/{}'.format(username))
def continue_pairwise(username, side_clicked):
# get the dataset for this user
dataset_user = utils_users.get_gp_dataset(
username, 'pairwise', num_objectives=specs_jobs.NUM_OBJECTIVES)
# initialise the acquirer which picks new datapoints
acquirer = DiscreteAcquirer(input_domain=utils_jobs.get_jobs(),
query_type='pairwise',
seed=specs_jobs.SEED)
# intialise the GP
gp = GPPairwise(num_objectives=specs_jobs.NUM_OBJECTIVES,
seed=specs_jobs.SEED)
# add collected datapoints to acquirer
acquirer.history = dataset_user.datapoints
# add collected datapoints to GP
gp.update(dataset_user)
# get the best job so far
job_best_idx = dataset_user.comparisons[-1, 0]
job_best = dataset_user.datapoints[job_best_idx]
# let acquirer pick new point
job_new = acquirer.get_next_point(gp, dataset_user)
# sort according to what user did last round
if side_clicked == "1":
job1 = job_best
job2 = job_new
else:
job1 = job_new
job2 = job_best
# transform the two jobs into dictionaries
job1 = utils_jobs.job_array_to_job_dict(job1)
job2 = utils_jobs.job_array_to_job_dict(job2)
# get the start time for this user
start_time = utils_users.get_experiment_start_time(username, 'pairwise')
if time.time() - start_time < specs_jobs.TIME_EXPERIMENT_SEC:
return render_template("query_pairwise_jobs.html",
username=username,
job1=job1,
job2=job2,
side_clicked=side_clicked)
else:
utils_users.update_experiment_status(username=username,
query_type='pairwise')
return redirect('start_experiment/{}'.format(username))
def submit_ranking():
# get the username and their dataset
username = request.form['username']
user_dataset = utils_users.get_gp_dataset(username, 'ranking', num_objectives=specs_traffic.NUM_OBJECTIVES)
# get the ranking the user submitted
ranking = np.fromstring(request.form['rankingResult'], sep=',', dtype=np.int)
# save it
utils_users.save_ranking(username, ranking)
# get the actual traffic (ranking returned the indiced in the dataset)
traffic_ranked = user_dataset.datapoints[ranking]
# add the ranking to the dataset
user_dataset.add_ranked_preferences(traffic_ranked)
print("comparisons from curr ranking", user_dataset.comparisons)
# save
utils_users.update_gp_dataset(username, user_dataset, 'ranking')
# get the start time for this user
utils_users.get_experiment_start_time(username, 'ranking')
button_type = request.form['buttonType']
if button_type == 'next':
return redirect(url_for('.start', username=username))
elif button_type == 'end':
# save end time
utils_users.save_experiment_end_time(username, 'ranking')
# register that this experiment was done
utils_users.update_experiment_status(username=username, query_type='ranking')
return redirect('start_experiment/{}'.format(username))
else:
raise NotImplementedError('Button type not defined')
def continue_pairwise(username, side_clicked, button_type):
# get the dataset for this user
dataset_user = utils_users.get_gp_dataset(
username, 'pairwise', num_objectives=specs_traffic.NUM_OBJECTIVES)
# initialise the acquirer which picks new datapoints
acquirer = DiscreteAcquirer(
input_domain=data_traffic.get_traffic_results(),
query_type='pairwise',
seed=specs_traffic.SEED)
# add collected datapoints to acquirer
acquirer.history = dataset_user.datapoints
# add virtual comparisons in first few queries
for i in range(np.min([dataset_user.datapoints.shape[0], 6])):
dataset_user.add_single_comparison(dataset_user.datapoints[i],
data_traffic.get_traffic_min())
dataset_user.add_single_comparison(data_traffic.get_traffic_max(),
dataset_user.datapoints[i])
# add linear prior in first few queries
if acquirer.history.shape[0] < 6:
prior_mean_type = 'linear'
else:
prior_mean_type = 'zero'
# intialise the GP
gp = GPPairwise(num_objectives=specs_traffic.NUM_OBJECTIVES,
seed=specs_traffic.SEED,
prior_mean_type=prior_mean_type)
# add collected datapoints to GP
gp.update(dataset_user)
# get the best job so far
job_best_idx = dataset_user.comparisons[-1, 0]
job_best = dataset_user.datapoints[job_best_idx]
# let acquirer pick new point
job_new = acquirer.get_next_point(gp, dataset_user)
# sort according to what user did last round
if side_clicked == "1":
item1 = job_best
item2 = job_new
else:
item1 = job_new
item2 = job_best
# get the names of the objectives
obj_names = data_traffic.get_objective_names()
obj_abbrev = data_traffic.get_objective_abbrev()
if button_type == 'next':
return render_template("query_pairwise_traffic.html",
username=username,
item1=-item1,
item2=-item2,
side_clicked=side_clicked,
obj_names=obj_names,
obj_abbrev=obj_abbrev)
elif button_type == 'end':
# save end time
utils_users.save_experiment_end_time(username, 'pairwise')
# register that this experiment was done
utils_users.update_experiment_status(username=username,
query_type='pairwise')
return redirect('start_experiment/{}'.format(username))
else:
raise NotImplementedError('Button type unknown.')