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process_cls_c_model.py
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# -*- coding: utf-8 -*-
# @Auther : Mingsong Li (lms-07)
# @Time : 2023-Apr
# @Address : Time Lab @ SDU
# @FileName : process_cls_c_model.py
# @Project : AMS-M2ESL (HSIC), IEEE TGRS
# # for IP and UP data sets, main processing file for SVM-RBF
import os
import time
import torch
import random
import numpy as np
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
import utils.evaluation as evaluation
import utils.data_load_operate as data_load_operate
import visual.cls_visual as cls_visual
time_current = time.strftime("%y-%m-%d-%H.%M", time.localtime())
# random seed setting
seed = 20
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.manual_seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
######## 0
model_list = ['SVM']
model_flag = 0
data_set_name_list = ['IP', 'UP', 'KSC', 'HU_tif']
data_set_name = data_set_name_list[1]
data_set_path = os.path.join(os.getcwd(), 'data')
# seed_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# seed_list=[0,1,2,3,4]
# seed_list=[0,1,2]
# seed_list=[0,1]
seed_list = [0]
# data set split
flag_list = [0, 1] # ratio or num
if data_set_name == 'IP':
ratio_list = [0.05, 0.005]
ratio = 5.0
elif data_set_name == 'UP':
ratio_list = [0.01, 0.001]
ratio = 1.0
num_list = [50, 0] # [train_num,val_num]
results_save_path = \
os.path.join(os.path.join(os.getcwd(), 'output/results'), model_list[model_flag] + str("_") +
data_set_name + str("_") + str(time_current) + str("_seed_") + str(seed) + str("_ratio_") + str(ratio))
cls_map_save_path = \
os.path.join(os.path.join(os.getcwd(), 'output/cls_maps'), model_list[model_flag] + str("_") +
data_set_name + str("_") + str(time_current) + str("_seed_") + str(seed) + str("_ratio_") + str(ratio))
if __name__ == '__main__':
data, gt = data_load_operate.load_data(data_set_name, data_set_path)
data = data_load_operate.standardization(data)
gt_reshape = gt.reshape(-1)
height, width, channels = data.shape
class_count = max(np.unique(gt))
OA_ALL = []
AA_ALL = []
KPP_ALL = []
EACH_ACC_ALL = []
Train_Time_ALL = []
Test_Time_ALL = []
CLASS_ACC = np.zeros([len(seed_list), class_count])
data_reshape = data.reshape(data.shape[0] * data.shape[1], -1)
for curr_seed in seed_list:
tic1 = time.perf_counter()
train_data_index, test_data_index, all_data_index = data_load_operate.sampling(ratio_list, num_list,
gt_reshape,
class_count, flag_list[0])
index = (train_data_index, test_data_index, all_data_index)
x_train, y_train, x_test, y_gt, x_all, y_all = data_load_operate.generate_data_set(data_reshape, gt_reshape,
index)
if model_flag == 0:
clf = SVC(kernel='rbf', gamma='scale', C=20, tol=1e-5, random_state=10).fit(x_train, y_train)
toc1 = time.perf_counter()
training_time = toc1 - tic1
Train_Time_ALL.append(training_time)
tic2 = time.perf_counter()
pred_test = clf.predict(x_test)
toc2 = time.perf_counter()
testing_time = toc2 - tic2
Test_Time_ALL.append(testing_time)
y_gt = gt_reshape[test_data_index] - 1
OA = metrics.accuracy_score(y_gt, pred_test)
confusion_matrix = metrics.confusion_matrix(pred_test, y_gt)
print("confusion_matrix\n{}".format(confusion_matrix))
ECA, AA = evaluation.AA_ECA(confusion_matrix)
kappa = metrics.cohen_kappa_score(pred_test, y_gt)
cls_report = evaluation.claification_report(y_gt, pred_test, data_set_name)
print("classification_report\n{}".format(cls_report))
# Visualization for all the labeled samples and total the samples
# total_pred = clf.predict(data_reshape)
# sample_list1 = [total_pred + 1]
# all_pred=clf.predict(x_all)
# sample_list2=[all_pred+1,all_data_index]
# cls_visual.gt_cls_map(gt,cls_map_save_path)
# cls_visual.pred_cls_map_cls(sample_list1,gt,cls_map_save_path)
# cls_visual.pred_cls_map_cls(sample_list2,gt,cls_map_save_path)
# Output infors
f = open(results_save_path + '_results.txt', 'a+')
str_results = '\n======================' \
+ "\nOA=" + str(OA) \
+ "\nAA=" + str(AA) \
+ '\nkpp=' + str(kappa) \
+ '\nacc per class:' + str(ECA) \
+ "\ntrain time:" + str(training_time) \
+ "\ntest time:" + str(testing_time) + "\n"
f.write(str_results)
f.write('{}'.format(confusion_matrix))
f.write('\n\n')
f.write('{}'.format(cls_report))
f.close()
OA_ALL.append(OA)
AA_ALL.append(AA)
KPP_ALL.append(kappa)
EACH_ACC_ALL.append(ECA)
OA_ALL = np.array(OA_ALL)
AA_ALL = np.array(AA_ALL)
KPP_ALL = np.array(KPP_ALL)
EACH_ACC_ALL = np.array(EACH_ACC_ALL)
Train_Time_ALL = np.array(Train_Time_ALL)
Test_Time_ALL = np.array(Test_Time_ALL)
np.set_printoptions(precision=4)
print("\n====================Mean result of {} times runs ==========================".format(len(seed_list)))
print('List of OA:', list(OA_ALL))
print('List of AA:', list(AA_ALL))
print('List of KPP:', list(KPP_ALL))
print('OA=', round(np.mean(OA_ALL) * 100, 2), '+-', round(np.std(OA_ALL) * 100, 2))
print('AA=', round(np.mean(AA_ALL) * 100, 2), '+-', round(np.std(AA_ALL) * 100, 2))
print('Kpp=', round(np.mean(KPP_ALL) * 100, 2), '+-', round(np.std(KPP_ALL) * 100, 2))
print('Acc per class=', np.mean(EACH_ACC_ALL, 0), '+-', np.std(EACH_ACC_ALL, 0))
print("Average training time=", round(np.mean(Train_Time_ALL), 2), '+-', round(np.std(Train_Time_ALL), 3))
print("Average testing time=", round(np.mean(Test_Time_ALL), 5), '+-', round(np.std(Test_Time_ALL), 5))
# Output infors
f = open(results_save_path + '_results.txt', 'a+')
str_results = '\n\n***************Mean result of ' + str(len(seed_list)) + 'times runs ********************' \
+ '\nList of OA:' + str(list(OA_ALL)) \
+ '\nList of AA:' + str(list(AA_ALL)) \
+ '\nList of KPP:' + str(list(KPP_ALL)) \
+ '\nOA=' + str(round(np.mean(OA_ALL) * 100, 2)) + '+-' + str(round(np.std(OA_ALL) * 100, 2)) \
+ '\nAA=' + str(round(np.mean(AA_ALL) * 100, 2)) + '+-' + str(round(np.std(AA_ALL) * 100, 2)) \
+ '\nKpp=' + str(round(np.mean(KPP_ALL) * 100, 2)) + '+-' + str(round(np.std(KPP_ALL) * 100, 2)) \
+ '\nAcc per class=' + str(np.mean(EACH_ACC_ALL, 0)) + '+-' + str(np.std(EACH_ACC_ALL, 0)) \
+ "\nAverage training time=" + str(round(np.mean(Train_Time_ALL), 2)) + '+-' + str(
round(np.std(Train_Time_ALL), 3)) \
+ "\nAverage testing time=" + str(round(np.mean(Test_Time_ALL), 5)) + '+-' + str(
round(np.std(Test_Time_ALL), 5))
f.write(str_results)
f.close()