def AC_(self, X, y):
# X, y = shuffle(X, Y)
# y = y.astype('int')
alibox = ToolBox(X=X, y=y, query_type='AllLabels')
alibox.split_AL(test_ratio=0.3, initial_label_rate=0.1, split_count=10)
model = alibox.get_default_model()
# stopping_criterion = alibox.get_stopping_criterion('num_of_queries',50)
model.fit(X, y)
pred = model.predict(X)
# 整理矩阵系数为信任度，返回start
w = model.class_weight
dim = w.shape[0]
trustValue = []
for i in range(0, dim):
value = math.exp(w[i]) # exp() 方法返回x的指数,ex。
trustValue.append(value)
return trustValue
import copy
from sklearn.datasets import load_iris
from alipy import ToolBox
X, y = load_iris(return_X_y=True)
alibox = ToolBox(X=X, y=y, query_type='AllLabels', saving_path='.')
# Split data
alibox.split_AL(test_ratio=0.3, initial_label_rate=0.1, split_count=10)
# Use the default Logistic Regression classifier
model = alibox.get_default_model()
# The cost budget is 50 times querying
stopping_criterion = alibox.get_stopping_criterion('num_of_queries', 50)
# Use pre-defined strategy
QBCStrategy = alibox.get_query_strategy(strategy_name='QueryInstanceQBC')
QBC_result = []
for round in range(10):
# Get the data split of one fold experiment
train_idx, test_idx, label_ind, unlab_ind = alibox.get_split(round)
# Get intermediate results saver for one fold experiment
saver = alibox.get_stateio(round)
while not stopping_criterion.is_stop():
# Select a subset of Uind according to the query strategy
# Passing model=None to use the default model for evaluating the committees' disagreement
select_ind = QBCStrategy.select(label_ind,
unlab_ind,
def create_and_implement_strategy(strategy_name, data, labels, queries):
# Keep only the values of data and labels dataframe (Later, we use the global split based on idxs)
X = data.values
y = np.asarray(labels)
toolbox = ToolBox(X=X, y=y, query_type='AllLabels', saving_path='.')
# Create Logistic Regression model ( Default Setting with liblinear solver)
model = toolbox.get_default_model()
# Implement query strategy
uncertainty_strategy = toolbox.get_query_strategy(strategy_name=strategy_name)
# Create array to save the results
examples = []
# Set stopping criterion, we will stop in 1000 labeled examples
stopping_criterion = toolbox.get_stopping_criterion('num_of_queries', queries)
# Get the indexes of the global split
with open("dataset_al", "rb") as f:
train_idx, test_idx, labeled_idx, unlabeled_idx = pickle.load(f)
# Create saver to save the results
saver = StateIO(round=0, train_idx=train_idx,
test_idx=test_idx, init_L=labeled_idx,
init_U=unlabeled_idx, saving_path='.')
# print(train_idx.shape, test_idx.shape)
# Starting with some labeled examples
model.fit(X=X[labeled_idx.index, :], y=y[labeled_idx.index])
y_pred = model.predict(X[test_idx, :])
# Calculate the accuracy of the prediction
accuracy = toolbox.calc_performance_metric(y_true=y[test_idx], y_pred=y_pred, performance_metric='accuracy_score')
# Save accuracy of the prediction
saver.set_initial_point(accuracy)
while not stopping_criterion.is_stop():
# Select example of the unlabeled dataset
example = uncertainty_strategy.select(labeled_idx, unlabeled_idx, model=model, batch_size=1)
# Update the label idxs
labeled_idx.update(example)
unlabeled_idx.difference_update(example)
# Train model for the added example
model.fit(X=X[labeled_idx.index, :], y=y[labeled_idx.index])
y_pred = model.predict(X[test_idx, :])
# Calculate accuracy
accuracy = toolbox.calc_performance_metric(y_true=y[test_idx], y_pred=y_pred,
performance_metric='accuracy_score')
# f1 = alibox.calc_performance_metric(y_true=y[test_idx], y_pred=y_pred, performance_metric='f1_score')
# Save update results
state = toolbox.State(select_index=example, performance=accuracy)
saver.add_state(state)
saver.save()
# Update progress for stopping criterion
stopping_criterion.update_information(saver)
stopping_criterion.reset()
examples.append(copy.deepcopy(saver))
# Uncomment and return in order to save the new active learning dataset
# Save selected x_train examples
X_train = X[labeled_idx, :]
# Save labels for the examples
y_train = y[labeled_idx, :]
# Reshape target
y_train = np.array(y_train).reshape(-1)
# Save to pickle
# with open('qbc_dataset','wb') as f:
# pickle.dump((X_train, y_train), f)
return examples
