def run(dataset):
n_features = len(meta[dataset]['val_name'])
result_online = Result('%s-%s' %(dataset, 'aws-online'), aws=True)
result_baseline = Result('%s-%s' %(dataset, 'aws-baseline'), aws=True)
result_active = Result('%s-%s' %(dataset, 'aws-active'), aws=True)
for repeat in range(0, n_repeat):
print 'Round %d of %d'% (repeat, n_repeat - 1)
ex = AWSOnline(meta[dataset]['model_id'], 1, 0, n_features, meta[dataset]['val_name'], ftype='uniform', error=.1)
test_x, test_y = load_svmlight_file('/Users/Fan/dev/ML/code/binary-classifiers/targets/%s/test.scale' % dataset, n_features)
test_x = test_x.todense()
test_y = [a if a == 1 else 0 for a in test_y]
train_x, train_y = [], []
for i in result_active.index:
q_by_u = result_active.Q_by_U[i]
print 'Active learning with budget %d / %d' % (q_by_u, q_by_u * (n_features + 1))
main = ActiveLearning(ex, (None, None), (test_x, test_y), n_features,
q_by_u * (n_features + 1), 5)
L_unif, L_test = main.do()
result_active.L_unif[i].append(L_unif)
result_active.L_test[i].append(L_test)
result_active.nquery[i].append(ex.get_n_query())
ex = AWSOnline(meta[dataset]['model_id'], 1, 0, n_features, meta[dataset]['val_name'], ftype='uniform', error=.1)
for i in result_online.index:
q_by_u = result_online.Q_by_U[i]
print 'collecting up to budget %d / %d' % (q_by_u, q_by_u * (n_features + 1))
ex.collect_up_to_budget(q_by_u * (n_features + 1))
train_x.extend(ex.pts_near_b)
train_y.extend(ex.pts_near_b_labels)
print 'retraining with %d points' % len(train_y)
# online
e = RBFKernelRetraining(ex.batch_predict, (train_x, train_y), (test_x, test_y), n_features)
L_unif, L_test = e.grid_retrain_in_x()
result_online.L_unif[i].append(L_unif)
result_online.L_test[i].append(L_test)
result_online.nquery[i].append(ex.get_n_query())
# baseline
e = Baseline(ex.batch_predict, (train_x, train_y), (test_x, test_y), n_features)
L_unif, L_test = e.do()
result_baseline.L_unif[i].append(L_unif)
result_baseline.L_test[i].append(L_test)
result_baseline.nquery[i].append(ex.get_n_query())
print result_online
print result_baseline
print result_active
for i in range(0, 1):
val_name = ['v1', 'v2', 'v3', 'v4', 'v5', 'v6', 'v7', 'v8', 'v9', 'v10']
n_features = 10
ex = AWSOnline(model_id='ml-lkYRYeldcrH',
label_p=1,
label_n=0,
n_features=n_features,
val_name=val_name,
ftype='uniform',
error=.1)
step = 6
train_x, train_y = [], []
val_x, val_y = [], []
test_x, test_y = load_svmlight_file('test.scale', n_features)
test_x = test_x.todense()
test_y = [a if a == 1 else 0 for a in test_y]
try:
while True:
ex.collect_pts(step)
train_x.extend(ex.pts_near_b)
train_y.extend(ex.pts_near_b_labels)
val_x.extend(ex.support_pts)
val_y.extend(ex.support_labels)
e = RBFKernelRetraining('circle', train_x, train_y, val_x, val_y,
test_x, test_y, n_features)
print ex.get_n_query(), e.grid_retrain_in_x()
except KeyboardInterrupt:
print 'Done'