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FOIT_csn.py
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# sklearn
from sklearn import svm
from sklearn.linear_model import LogisticRegression
from sklearn.calibration import CalibratedClassifierCV
from sklearn.utils import shuffle
# from sklearn.metrics import log_loss
# indexes of N largest numbers
import heapq
# model storage
import joblib
# standard package
import numpy as np
import time
# utils
import utils
# time and acc
temp_c0 = []
temp_c0u = []
temp_foit = []
temp_time_baseline = []
temp_time_updated_based = []
temp_time_FOIT = []
#
cd_count = 0
dataset_name = 'seed4'
if dataset_name == 'seed3':
cd_count = 9
elif dataset_name == 'seed4':
cd_count = 16
else:
print("Unexcepted dataset name!")
raise EnvironmentError
number_trial, number_label, labels= utils.get_number_of_label_n_trial(dataset_name)
ses_data, ses_label = utils.load_by_subject(dataset_name) # 15*2*(m*310)
for sub_number in range(15):
print("Sub id: ", sub_number)
# cross-session 取session 3
cd_data, cd_label, ud_data, ud_label = utils.pick_one_data(dataset_name, session_id=2, cd_count=cd_count, sub_id=sub_number)
ses_data_sub, ses_label_sub = ses_data[sub_number], ses_label[sub_number] # 2*(m*310)
ses_data_sub, ses_label_sub = shuffle(ses_data_sub, ses_label_sub, random_state=0)
cd_data, cd_label = shuffle(cd_data, cd_label, random_state=0)
ud_data, ud_label = shuffle(ud_data, ud_label, random_state=0)
'''
a)
'''
clf = svm.LinearSVC(max_iter=10000)
clf = CalibratedClassifierCV(clf, cv=5)
since = time.time()
clf.fit(utils.normalization(cd_data), cd_label.squeeze())
time_elapsed_baseline = time.time() - since
print('Baseline training complete in {:.4f}s'.format(time_elapsed_baseline))
scoreA = utils.test(clf, utils.normalization(ud_data), ud_label.squeeze())
print("Baseline score: ", scoreA)
temp_time_baseline.append(time_elapsed_baseline)
temp_c0.append(scoreA)
'''
b)
'''
accs = [] # two classifiers from the reservoir for each sub
clf_sources = []
for j in range(2):
path = "models/" + dataset_name + "/csn/sub" + str(sub_number) + "/lr" + str(j) + ".m"
temp_clf = joblib.load(path)
clf_sources.append(temp_clf)
score = utils.test(temp_clf, utils.normalization(ud_data), ud_label.squeeze())
accs.append(score)
print("Accs of classifiers: {}".format(accs))
# accs = utils.normalization(accs)
'''
c)
'''
rho = 2
s_data_all, s_label_all = utils.stack_list(ses_data_sub, ses_label_sub)
s_data_all_predict_proba = clf.predict_proba(utils.normalization(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 j in range(s_label_all_proba.shape[0]):
confidence[j] = s_label_all_proba[j].dot(s_data_all_predict_proba[j].T)
# print(confidence.shape)
# subs_data_0, subs_data_1, subs_data_2, subs_data_3 = [], [], [], []
# conf_0, conf_1, conf_2, conf_3 = [],[],[],[]
# subs_label_0, subs_label_1, subs_label_2, subs_label_3 = [],[],[],[]
# for j in range(len(s_data_all)):
# temp_label = s_label_all[j][0]
# eval('subs_data_' + str(temp_label)).append(s_data_all[j])
# eval('conf_' + str(temp_label)).append(confidence[j])
# eval('subs_label_' + str(temp_label)).append(s_label_all[j])
# indices = []
# for j in range(4):
# indices.append(np.argsort(eval('conf_'+str(j)), axis=0)[::-1])
# topK_indices = [indices[j][:int(rho*len(cd_label)/4)] for j in range(len(indices))]
# S_data = None
# S_label = None
# for k in range(len(topK_indices)):
# for j in topK_indices[k]:
# temp_conf = eval('conf_'+str(k))[j[0]]
# one_data = eval('subs_data_'+str(k))[j[0]]
# one_label = eval('subs_label_'+str(k))[j[0]]
# 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
# print(len(cd_label))
# print(S_data.shape)
# print(S_label.shape)
### without balance
# indices = np.argsort(confidence, axis=0)
indices = np.argsort(confidence, axis=0)[::-1]
topK_indices = indices[:int(rho*len(cd_label))]
S_data = None
S_label = None
for i in topK_indices:
# print(confidence[i])
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
# print(S_data.shape)
# print(S_label.shape)
'''
d)
'''
print(utils.count_for_array(cd_label))
L_S_data = cd_data.copy()
L_S_label = cd_label.copy()
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)
print(utils.count_for_array(L_S_label))
clf.fit(utils.normalization(L_S_data), L_S_label.squeeze())
time_updated_baseline = time.time() - since
print('Updated baseline training complete in {:.4f}s'.format(time_updated_baseline))
temp_time_updated_based.append(time_updated_baseline)
scoreD = utils.test(clf, utils.normalization(ud_data), ud_label.squeeze())
print("Updated model score: {}".format(scoreD))
temp_c0u.append(scoreD)
'''
e)
'''
weight = 2
proba_result_all = clf.predict_proba(utils.normalization(ud_data)) * weight
weight_for_clfs = utils.decide_which_clf_to_use(scoreD, accs)
# weight_for_clfs = utils.decide_which_clf_to_use(scoreA, accs)
# weight_for_clfs = [1, 1]
print(weight_for_clfs)
# print(accs)
for j in range(len(weight_for_clfs)):
proba_result_all += clf_sources[j].predict_proba(utils.normalization(ud_data)) * weight_for_clfs[j]
# max_index = np.argmax(accs)
# proba_result_all += clf_sources[max_index].predict_proba(utils.normalization(ud_data)) * 1
corrects = np.sum(np.argmax(proba_result_all, axis=1) == ud_label.squeeze())
since_FOIT = time.time() - since
print('FOIT training complete in {:.4f}s'.format(since_FOIT))
temp_time_FOIT.append(since_FOIT)
scoreE = corrects/len(ud_label)
print("Ensembled model score: {}".format(scoreE))
temp_foit.append(scoreE)
# count = 1
# threshold = utils.find_threshold(accs)
# print("249: ", np.median(accs) - np.mean(accs))
# print("250: ", np.median(accs)/np.mean(accs))
# print("Threshold: ", threshold)
# for i in range(len(clf_sources)):
# if accs[i] > threshold:
# count += 1
# proba_result_all += clf_sources[i].predict_proba(utils.normalization(ud_data)) * accs[i]
# proba_result_all = proba_result_all / count
# corrects = np.sum(np.argmax(proba_result_all, axis=1) == ud_label.squeeze())
# since_FOIT = time.time() - since
# print('FOIT training complete in {:.4f}s'.format(since_FOIT))
# temp_time_FOIT.append(since_FOIT)
# temp_foit.append(corrects/len(ud_label))
print(temp_c0)
print(temp_c0u)
print(temp_foit)
print("A: ", np.mean(temp_c0), np.std(temp_c0))
print("D: ", np.mean(temp_c0u), np.std(temp_c0u))
print("E: ", np.mean(temp_foit), np.std(temp_foit))
print("Time cost for training baseline: ", np.mean(temp_time_baseline))
print("Time cost for training updated baseline: ", np.mean(temp_time_updated_based))
print("Time cost for training FOIT: ", np.mean(temp_time_FOIT))