def al_qbc(self, data, target, X_train, y_train, X_full, y_full, train_idx,
committee_strategy):
acc = []
pre = []
rec = []
fs = []
X_pool = deepcopy(X_full)
y_pool = deepcopy(y_full)
# initializing Committee members
n_members = 2
learner_list = list()
for member_idx in range(n_members):
# initial training data
# n_initial = 5
# train_idx = np.random.choice(range(X_pool.shape[0]), size=n_initial, replace=False)
# X_train = X_pool[train_idx]
# y_train = y_pool[train_idx]
# creating a reduced copy of the data with the known instances removed
X_pool = np.delete(X_pool, train_idx, axis=0)
y_pool = np.delete(y_pool, train_idx)
# initializing learner
learner = ActiveLearner(
estimator=RandomForestClassifier(),
# query_strategy=vote_entropy_sampling,
X_training=X_train,
y_training=y_train)
learner_list.append(learner)
# assembling the committee
committee = Committee(learner_list=learner_list,
query_strategy=committee_strategy)
# print('Committee initial predictions, accuracy = %1.3f' % committee.score(data, target))
# print('%1.3f' % committee.score(data, target))
n_queries = self.query_number
for idx in range(n_queries):
query_idx, query_instance = committee.query(X_pool)
committee.teach(X=X_pool[query_idx].reshape(1, -1),
y=y_pool[query_idx].reshape(1, ))
# remove queried instance from pool
X_pool = np.delete(X_pool, query_idx, axis=0)
y_pool = np.delete(y_pool, query_idx)
# learner_score = committee.score(data, target)
# print('Committee %d th query predictions, accuracy = %1.3f' % (idx , learner_score))
precision, recall, fscore, support, accuracy = self.performance_measure(
learner, X_full, y_full)
learner_score = accuracy
acc.append(learner_score)
pre.append(precision)
rec.append(recall)
fs.append(fscore)
print('%0.3f' % (learner_score), end=",")
return fs
def al_qbc(data, target, X_train, y_train, X_full, y_full, train_idx):
# print("START: Q")
X_pool = deepcopy(X_full)
y_pool = deepcopy(y_full)
# initializing Committee members
n_members = 2
learner_list = list()
for member_idx in range(n_members):
# initial training data
# n_initial = 5
# train_idx = np.random.choice(range(X_pool.shape[0]), size=n_initial, replace=False)
# X_train = X_pool[train_idx]
# y_train = y_pool[train_idx]
# creating a reduced copy of the data with the known instances removed
X_pool = np.delete(X_pool, train_idx, axis=0)
y_pool = np.delete(y_pool, train_idx)
# initializing learner
learner = ActiveLearner(
estimator=RandomForestClassifier(),
X_training=X_train, y_training=y_train
)
learner_list.append(learner)
# assembling the committee
committee = Committee(learner_list=learner_list)
# print('Committee initial predictions, accuracy = %1.3f' % committee.score(data, target))
# print('%1.3f' % committee.score(data, target))
n_queries = 1500
for idx in range(n_queries):
query_idx, query_instance = committee.query(X_pool)
committee.teach(
X=X_pool[query_idx].reshape(1, -1),
y=y_pool[query_idx].reshape(1, )
)
# remove queried instance from pool
X_pool = np.delete(X_pool, query_idx, axis=0)
y_pool = np.delete(y_pool, query_idx)
learner_score = committee.score(data, target)
# print('Committee %d th query predictions, accuracy = %1.3f' % (idx , learner_score))
print('%0.3f' % (learner_score), end=",")
