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altest.py
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'''
Author: your name
Date: 2020-08-12 02:43:23
LastEditTime: 2020-08-27 05:48:25
LastEditors: Please set LastEditors
Description: In User Settings Edit
FilePath: /FOIT/al.py
'''
'''
学习引擎：svm
选择引擎： 信息熵or点积？（minimum）
人工oracle 给100%acc label
'''
'''
Step 1: get labelled data, unlabelled data and test data.
'''
from sklearn import svm
from sklearn.calibration import CalibratedClassifierCV
from sklearn.utils import shuffle
from sklearn.metrics import log_loss
# package
import numpy as np
import time
import utils
def al(dataset_name='seed4', FOIT_type='cross-all', rounds=10, batch_size=50):
data, label = utils.load_source_data(dataset_name=dataset_name, FOIT_type=FOIT_type)
_, number_label, _ = utils.get_number_of_label_n_trial(dataset_name)
# data, label = utils.load_session_data_label(dataset_name, 0) # as unlabelled data
cd_count = 16 if dataset_name=='seed4' else 9 if dataset_name=='seed3' else print('Wrong dataset_name')
iteration_number = 3 if FOIT_type=='cross-subject' else 15
accs = [([]) for i in range(iteration_number)]
times = [([]) for i in range(iteration_number)]
for ite in range(iteration_number):
session_id = -1
sub_id = -1
if FOIT_type == 'cross-subject':
session_id = ite
sub_id = 14
elif FOIT_type == 'cross-session':
session_id = 2
sub_id = ite
elif FOIT_type == 'cross-all':
session_id = 1
sub_id = ite
else:
print('Wrong FOIT type!')
# print("Ite: ", ite)
cd_data, cd_label, ud_data, ud_label = utils.pick_one_data(dataset_name, session_id=session_id, cd_count=cd_count, sub_id=sub_id)
cd_data, cd_label = shuffle(cd_data, cd_label, random_state=0)
ud_data, ud_label = shuffle(ud_data, ud_label, random_state=0)
cd_data_min, cd_data_max = np.min(cd_data), np.max(cd_data)
cd_data = utils.normalization(cd_data) # labelled data
ud_data = utils.normalization(ud_data) # test data
if FOIT_type == 'cross-all':
data_ite, label_ite = data.copy(), label.copy()
for i in range(len(data)):
data_ite[i], label_ite[i] = shuffle(data_ite[i], label_ite[i], random_state=0)
# data_ite, label_ite = shuffle(data, label, random_state=0)
for i in range(len(data)):
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min, cd_data_max)
# data_ite = utils.normalization(data_ite)
elif FOIT_type == 'cross-session':
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i], label_ite[i], random_state=0)
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min, cd_data_max)
# data_ite = utils.normalization(data_ite)
else:
data_ite, label_ite = data[ite], label[ite]
for i in range(len(data_ite)):
data_ite[i], label_ite[i] = shuffle(data_ite[i], label_ite[i], random_state=0)
# data_ite, label_ite = shuffle(data_ite, label_ite, random_state=0)
for i in range(len(data_ite)):
# data_ite[i] = utils.normalization(data_ite[i])
data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min, cd_data_max)
# data_ite, label_ite = data.copy(), label.copy()
# for i in range(len(data)):
# data_ite[i], label_ite[i] = shuffle(data_ite[i], label_ite[i], random_state=0)
# for i in range(len(data)):
# data_ite[i] = utils.norm_with_range(data_ite[i], cd_data_min, cd_data_max)
# baseline
clf = svm.LinearSVC(max_iter=30000)
clf = CalibratedClassifierCV(clf, cv=5)
since = time.time()
clf.fit(cd_data, cd_label.squeeze())
time_baseline = time.time() - since
scoreA = utils.test(clf, ud_data, ud_label.squeeze())
accs[ite].append(scoreA)
times[ite].append(time_baseline)
# select the data from the reservoir iteratively
s_data_all, s_label_all = utils.stack_list(data_ite, label_ite)
L_S_data = None
L_S_label = None
for i in range(rounds):
# print("Rounds: ", i)
# print(type(s_data_all))
# print(s_data_all.shape)
s_data_all_predict_proba = clf.predict_proba(s_data_all)
s_label_all_proba = utils.get_one_hot(s_label_all.squeeze(), number_label)
confidence = np.zeros((s_label_all_proba.shape[0], 1))
for i in range(s_label_all_proba.shape[0]):
confidence[i] = s_label_all_proba[i].dot(s_data_all_predict_proba[i].T)
# confidence[i] = log_loss(s_label_all_proba[i], s_data_all_predict_proba[i])
indices = np.argsort(confidence, axis=0) # take the minimum topK indices
topK_indices = indices[:batch_size]
S_data = None
S_label = None
for i in topK_indices:
one_data = s_data_all[i]
one_label = s_label_all[i]
if S_data is not None:
S_data = np.vstack((S_data, one_data))
S_label = np.vstack((S_label, one_label))
else:
S_data = one_data
S_label = one_label
for i in range(len(s_data_all)-1, -1, -1):
if i in topK_indices:
s_data_all = np.delete(s_data_all, i, axis=0)
s_label_all = np.delete(s_label_all, i, axis=0)
if L_S_data is None:
L_S_data = cd_data.copy()
L_S_label = cd_label.copy()
else:
pass
L_S_data = np.vstack((L_S_data, S_data))
L_S_label = np.vstack((L_S_label, S_label))
L_S_data, L_S_label = shuffle(L_S_data, L_S_label, random_state=0)
clf.fit(L_S_data, L_S_label.squeeze())
time_updated_time = time.time() - since
times[ite].append(time_updated_time)
scoreTMP = utils.test(clf, ud_data, ud_label.squeeze())
accs[ite].append(scoreTMP)
ResultTime = []
ResultAcc = []
ResultStd = []
for i in range(rounds+1):
tmpTime = []
tmpAcc = []
for j in range(iteration_number):
tmpTime.append(times[j][i])
tmpAcc.append(accs[j][i])
ResultTime.append(np.mean(tmpTime))
ResultAcc.append(np.mean(tmpAcc))
ResultStd.append(np.std(tmpAcc))
print("Time: ", ResultTime)
print("Accs: ", ResultAcc)
print("Stds: ", ResultStd)
if __name__ == "__main__":
FOIT_type_all = ['cross-all', 'cross-session', 'cross-subject']
dataset_name_all = ['seed4', 'seed3']
# FOIT_type_all = ['cross-all']
# dataset_name_all = ['seed4']
for dataset_name in dataset_name_all:
print('Dataset name: {}'.format(dataset_name))
print('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')
for FOIT_type in FOIT_type_all:
print('FOIT type: {}'.format(FOIT_type))
batch_size = 0
if FOIT_type=='cross-session' and dataset_name=='seed4':
batch_size = 150
else:
batch_size = 250
al(dataset_name=dataset_name, FOIT_type=FOIT_type, rounds=10, batch_size=batch_size)
# a = [3, 4, 2, 7, 5, 9, 0, 1, 6, 8]
# tmp = np.argsort(a, axis=0)
# tmp = tmp[0:3]
# print(tmp)
# for i in range(len(a)-1, -1, -1):
# if i in tmp:
# a = np.delete(a, i)
# print(a)