def test_stop3():
assert not stop3.is_stop()
stop3.update_information(example_saver)
example_saver_local = copy.deepcopy(example_saver)
assert stop3._accum_cost == 0
example_saver_local.add_state(State(select_index=[2], performance=0.89, cost=[3]))
stop3.update_information(example_saver_local)
assert stop3._accum_cost == 3
assert not stop3.is_stop()
example_saver_local.add_state(State(select_index=[3], performance=0.89, cost=[7]))
stop3.update_information(example_saver_local)
assert stop3._accum_cost == 10
assert stop3.is_stop()
def test_stop4():
assert not stop4.is_stop()
stop4.update_information(example_saver)
example_saver_local = copy.deepcopy(example_saver)
assert stop4._percent == 0
example_saver_local.add_state(State(select_index=[2], performance=0.89, cost=[3]))
stop4.update_information(example_saver_local)
assert stop4._percent == 1/6
assert stop4.is_stop()
def test_stop2():
assert not stop2.is_stop()
stop2.update_information(example_saver)
example_saver_local = copy.deepcopy(example_saver)
assert stop2._current_iter == 0
example_saver_local.add_state(State(select_index=[2], performance=0.89))
stop2.update_information(example_saver_local)
assert stop2._current_iter == 1
assert not stop2.is_stop()
stop2._current_iter = 10
assert stop2.is_stop()
def target_func(round, train_id, test_id, Lcollection, Ucollection, saver, examples, labels, global_parameters):
# your query strategy
qs = QueryInstanceQBC(examples, labels, disagreement='vote_entropy')
# your model
reg = linear_model.LogisticRegression(solver='liblinear')
reg.fit(X=examples[Lcollection.index, :], y=labels[Lcollection.index])
# stopping criterion
while len(Ucollection) > 30:
select_index = qs.select(Lcollection, Ucollection, reg, n_jobs=1)
Ucollection.difference_update(select_index)
Lcollection.update(select_index)
# update model
reg.fit(X=examples[Lcollection.index, :], y=labels[Lcollection.index])
pred = reg.predict(examples[test_id, :])
accuracy = sum(pred == labels[test_id]) / len(test_id)
# save intermediate results
st = State(select_index=select_index, performance=accuracy)
saver.add_state(st)
saver.save()
def run_thread(round, train_id, test_id, Lcollection, Ucollection, saver, examples, labels, global_parameters):
# initialize object
reg.fit(X=examples[Lcollection.index, :], y=labels[Lcollection.index])
pred = reg.predict(examples[test_id, :])
accuracy = sum(pred == labels[test_id]) / len(test_id)
# initialize StateIO module
saver.set_initial_point(accuracy)
while len(Ucollection) > 30:
select_index = qs.select(Lcollection, Ucollection, model=reg)
Ucollection.difference_update(select_index)
Lcollection.update(select_index)
# update model
reg.fit(X=examples[Lcollection.index, :], y=labels[Lcollection.index])
pred = reg.predict(examples[test_id, :])
accuracy = sum(pred == labels[test_id]) / len(test_id)
# save intermediate results
st = State(select_index=select_index, performance=accuracy)
# add user defined information
# st.add_element(key='sub_ind', value=sub_ind)
saver.add_state(st)
saver.save()
train_ind, test_ind, L_ind, U_ind = toolbox.get_split(round=0)
# -------Initialize StateIO----------
saver = StateIO(round=0,
train_idx=train_ind,
test_idx=test_ind,
init_L=L_ind,
init_U=U_ind,
saving_path='.')
# or by using toolbox
# saver = toolbox.get_stateio(round=0)
saver.init_L.difference_update([0, 1, 2])
saver.init_U.update([0, 1, 2])
# -------Basic operations------------
st1_batch1 = State(select_index=[1], performance=0.89)
my_value = 'my_entry_info'
st1_batch1.add_element(key='my_entry', value=my_value)
st1_batch2 = State(select_index=[0, 1], performance=0.89)
st2_batch1 = State(select_index=[0], performance=0.89)
st3_batch1 = State(select_index=[2], performance=0.89)
saver.add_state(st1_batch1)
saver.add_state(st1_batch2)
saver.add_state(st2_batch1)
saver.save()
prev_st = saver.get_state(index=1) # get 2nd query
# or use the index operation directly
prev_st = saver[1]
selected_feature = strategy.select(observed_entries=label_ind,
unkonwn_entries=unlab_ind)
# update index
label_ind.update(selected_feature)
unlab_ind.difference_update(selected_feature)
# train/test
lab_in_train = map_whole_index_to_train(train_idx, label_ind)
X_mc = AFASMC_mc(X=X[train_idx], y=y[train_idx], omega=lab_in_train)
model.fit(X_mc, y[train_idx])
pred = model.predict(X[test_idx])
perf = accuracy_score(y_true=y[test_idx], y_pred=pred)
# save
st = State(select_index=selected_feature, performance=perf)
saver.add_state(st)
# saver.save()
stopping_criterion.update_information(saver)
stopping_criterion.reset()
AFASMC_result.append(copy.deepcopy(saver))
SVD_mc = IterativeSVD_mc(rank=4)
# Stablility
for i in range(5):
train_idx = tr[i]
test_idx = te[i]
label_ind = MultiLabelIndexCollection(lab[i], label_size=X.shape[1])
unlab_ind = MultiLabelIndexCollection(unlab[i], label_size=X.shape[1])
from alipy.toolbox import ToolBox as acebox
X, y = load_iris(return_X_y=True)
split_count = 5
cur_path = os.path.abspath('.')
toolbox = acebox(X=X, y=y, query_type='AllLabels', saving_path=cur_path)
# split data
toolbox.split_AL(test_ratio=0.3, initial_label_rate=0.1, split_count=split_count)
saver = toolbox.get_stateio(round=0)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
saver.init_L.difference_update([0, 1, 2])
saver.init_U.update([0, 1, 2])
st1_batch2 = State(select_index=[0, 1], performance=0.89)
st1_batch1 = State(select_index=[1], performance=0.89)
st2_batch1 = State(select_index=[0], performance=0.89)
st3_batch1 = State(select_index=[2], performance=0.89)
def test_stateio_validity_checking():
saver.add_state(st1_batch1)
saver.add_state(st1_batch2)
saver.add_state(st2_batch1)
assert not saver.check_batch_size()
assert saver.cost_inall == 0
nq, cost = saver.refresh_info()
assert nq == 4
assert cost == 0
