def model_evaluate(self):
# evaluate the model
## preprocess: find test_users and candidate_items
test_users = np.unique(self.test_tuple[:,0]) # all users in the test set
allItems_train = np.unique(self.train_tuple[:,1]) # all items in the train set
allItems_test = np.unique(self.test_tuple[:,1]) # all items in the test set
candidate_items = np.union1d(allItems_train,allItems_test) # all items in the train and test set
recEval = Evaluate(self.userFactors,self.itemFactors,self.train_matrix,self.test_matrix,test_users,candidate_items)
ret = recEval.CalcMetrics()
print 'AUC =',ret[0],'Prec@5 =',ret[1],'Prec@10 =',ret[2], 'MAP =', ret[3], 'Rec@5 =', ret[4], 'Rec@10 =', ret[5], 'NDCG =', ret[6], 'MRR =', ret[7]
return ret
def Utility(GT_path, evaluated_path,num_path):
'''
Function to evaulate the your resutls for SBMnet dataset, this code will generate a 'cm.txt' file in your result path to save all the metrics.
input: GT_path: the path of the groundtruth folder.
evaluated_path: the path of your evaluated results folder.
'''
result_file = os.path.join(evaluated_path, 'cm.txt')
with open(result_file, 'w') as fid:
fid.write('\t\timage_name\tPSNR\tSSIM\tMSE\tMAE\r\n')
m_AGE = 0
m_pEPs = 0
m_pCEPs = 0
m_MSSSIM = 0
m_PSNR = 0
m_SSIM = 0
m_MSE = 0
m_MAE = 0
# ipdb.set_trace()
c_AGE = 0
c_pEPs = 0
c_pCEPs = 0
c_MSSSIM = 0
c_PSNR = 0
c_SSIM = 0
c_MSE = 0
c_MAE = 0
image_num = 0
for root, dirs, files in os.walk(evaluated_path):
MSSSIM_max = 0
for i in files:
# 判断是否以.jpg结尾
if i.endswith('.JPG') or i.endswith('.jpg') or i.endswith('.PNG') or i.endswith('.png'):
picname = i.split('.')[0]
print("picname:",picname)
num = picname.split('_')[0]
#if more than one GT exists for the video, we keep the
#metrics with the highest MSSSIM value.
if(num_path==2000):
GT_img = scipy.misc.imread(GT_path+num+".jpg") #background ground truth
result_img = scipy.misc.imread(evaluated_path+num+".jpg")
if(num_path==1080):
GT_img = scipy.misc.imread(GT_path+num+"_gt.png") #background ground truth
result_img = scipy.misc.imread(evaluated_path+picname+".png")
AGE, pEPs, pCEPs, MSSSIM, PSNR, SSIM, MSE,MAE = Evaluate.Evaluate(GT_img, result_img);
if MSSSIM > MSSSIM_max:
MSSSIM_max = MSSSIM
v_AGE = AGE
v_pEPs = pEPs
v_pCEPs = pCEPs
v_MSSSIM = MSSSIM
v_PSNR = PSNR
v_SSIM = SSIM
v_MSE = MSE
v_MAE = MAE
#save the video evaluation results
with open(result_file, 'a+') as fid:
fid.write('\t\t' + picname + ':\t' + str(round(v_PSNR, 4)) + '\t' + str(round(v_SSIM, 4)) + '\t' + str(round(v_MSE, 4)) + '\t' + str(round(v_MAE, 4)) + '\r\n')
c_AGE = c_AGE + v_AGE
c_pEPs = c_pEPs + v_pEPs
c_pCEPs = c_pCEPs + v_pCEPs
c_MSSSIM = c_MSSSIM + v_MSSSIM
c_PSNR = c_PSNR + v_PSNR
c_SSIM = c_SSIM + v_SSIM
c_MSE = c_MSE + v_MSE
c_MAE = c_MAE + v_MAE
image_num = image_num + 1
c_AGE = c_AGE / float(image_num)
c_pEPs = c_pEPs / float(image_num)
c_pCEPs = c_pCEPs / float(image_num)
c_MSSSIM = c_MSSSIM / float(image_num)
c_PSNR = c_PSNR / float(image_num)
c_SSIM = c_SSIM / float(image_num)
c_MSE = c_MSE / float(image_num)
c_MAE = c_MAE / float(image_num)
#save the category evaluation results
with open(result_file, 'a+') as fid:
fid.write('\t\timage_name\tPSNR\tSSIM\tMSE\tMAE\r\n')
fid.write('\r\n' + 'gt' + '_AVG::\t\t' + str(round(c_PSNR, 4)) + '\t' + str(round(c_SSIM, 4)) + '\t' + str(round(c_MSE, 4))+ '\t' + str(round(c_MAE, 4)) + '\r\n\r\n')
m_AGE = m_AGE + c_AGE
m_pEPs = m_pEPs + c_pEPs
m_pCEPs = m_pCEPs + c_pCEPs
m_MSSSIM = m_MSSSIM + c_MSSSIM
m_PSNR = m_PSNR + c_PSNR
m_SSIM = m_SSIM + c_SSIM
m_MSE = m_MSE + c_MSE
m_MAE = m_MAE + c_MAE
with open(result_file, 'a+') as fid:
fid.write('Total:\t\t\t' + str(round(m_PSNR, 4)) + '\t' + str(round(m_SSIM*100, 4)) + '\t' + str(round(m_MSE, 4)) + '\t' + str(round(m_MAE*100, 4)) + '\r\n')
essay = Essay(text, prompt, grade, stop_words)
essay.set_words()
#essay.get_tagged()
essay.grammar = grammar
#print(files)
c1, a = essay.get_length()
b = essay.get_spellingmistakes()
#c1 = essay.get_sv_agreement()
c2 = essay.get_verb_usage()
c3 = essay.get_sentence_formation()
d1 = essay.get_coherence()
d2 = essay.get_topic_relevance()
evaluate = Evaluate(a, b, c1, c2, c3, d1, d2)
final_score = evaluate.get_score()
if not files in results_dict:
results_dict[files] = [
a, b, c1, c2, c3, d1, d2, final_score, grade
]
# Dump raw values to csv
with open('../dump/results.csv', 'w') as csv_file:
writer = csv.writer(csv_file)
for key, value in results_dict.items():
li = []
li.append(key)
for v in value:
li.append(v)
args = parser.parse_args()
stock_code = args.code
expect_tag = args.label
method = args.method
stockcodes_list = ['000001']
filenames_list = ["5min/000001.csv"]
expect_day = '2018-01-18'
his_num = 5
# print(stock_code)
fast_data_searcher = FastResearchData(stock_code, stockcodes_list,
filenames_list)
stock_data = fast_data_searcher.run()
# calculator = CalCorrMatrix()
data_preparer = PreProcessor(stock_data, expect_day, expect_tag, his_num)
valid_set, train_set, valid_tag, train_tag = data_preparer.run()
regress = Regression(valid_set, train_set, valid_tag, train_tag, method)
pred_result = regress.run()
print(pred_result)
evaluator = Evaluate(valid_set, valid_tag, pred_result, expect_tag, method)
evaluator.run()
drawer = PicDrawer(method, valid_tag, pred_result)
drawer.picDrawer()
def Utility(GT_path, evaluated_path):
'''
Function to evaulate the your resutls for SBMnet dataset, this code will generate a 'cm.txt' file in your result path to save all the metrics.
input: GT_path: the path of the groundtruth folder.
evaluated_path: the path of your evaluated results folder.
'''
category_list = [
'backgroundMotion', 'basic', 'clutter', 'illuminationChanges',
'intermittentMotion', 'jitter', 'veryLong', 'veryShort'
]
category_num = len(category_list)
result_file = os.path.join(evaluated_path, 'cm.txt')
with open(result_file, 'w') as fid:
fid.write('\t\tvideo\tAGE\tpEPs\tpCEPs\tMSSSIM\tPSNR\tCQM\r\n')
m_AGE = 0
m_pEPs = 0
m_pCEPs = 0
m_MSSSIM = 0
m_PSNR = 0
m_CQM = 0
ipdb.set_trace()
for category in category_list:
print(category)
c_AGE = 0
c_pEPs = 0
c_pCEPs = 0
c_MSSSIM = 0
c_PSNR = 0
c_CQM = 0
GT_category_path = os.path.join(GT_path, category)
evaluated_category_path = os.path.join(evaluated_path, category)
video_num = 0
with open(result_file, 'a+') as fid:
fid.write(category[0:min(8, len(category))] + ': \r\n')
for video in os.listdir(GT_category_path):
GT_video_path = os.path.join(GT_category_path, video)
GTs = os.listdir(os.path.join(GT_video_path))
GT_exist = False
MSSSIM_max = 0
for file in GTs:
if file.endswith('.jpg'):
GT_exist = True
#if more than one GT exists for the video, we keep the
#metrics with the highest MSSSIM value.
GT_img = scipy.misc.imread(
os.path.join(GT_video_path,
file)) #background ground truth
evaluated_video_path = os.path.join(
evaluated_category_path, video)
files = os.listdir(os.path.join(evaluated_video_path))
for file in files: #read the first image in the video folder
if file.endswith('.jpg'):
result_img = scipy.misc.imread(
os.path.join(evaluated_video_path, file))
break
ipdb.set_trace()
AGE, pEPs, pCEPs, MSSSIM, PSNR, CQM = Evaluate.Evaluate(
GT_img, result_img)
if MSSSIM > MSSSIM_max:
v_AGE = AGE
v_pEPs = pEPs
v_pCEPs = pCEPs
v_MSSSIM = MSSSIM
v_PSNR = PSNR
v_CQM = CQM
MSSSIM_max = MSSSIM
if GT_exist:
#save the video evaluation results
with open(result_file, 'a+') as fid:
fid.write('\t\t' + video[0:min(5, len(video))] + ':\t' +
str(round(v_AGE, 4)) + '\t' +
str(round(v_pEPs, 4)) + '\t' +
str(round(v_pCEPs, 4)) + '\t' +
str(round(v_MSSSIM, 4)) + '\t' +
str(round(v_PSNR, 4)) + '\t' +
str(round(v_CQM, 4)) + '\r\n')
c_AGE = c_AGE + v_AGE
c_pEPs = c_pEPs + v_pEPs
c_pCEPs = c_pCEPs + v_pCEPs
c_MSSSIM = c_MSSSIM + v_MSSSIM
c_PSNR = c_PSNR + v_PSNR
c_CQM = c_CQM + v_CQM
video_num = video_num + 1
c_AGE = c_AGE / float(video_num)
c_pEPs = c_pEPs / float(video_num)
c_pCEPs = c_pCEPs / float(video_num)
c_MSSSIM = c_MSSSIM / float(video_num)
c_PSNR = c_PSNR / float(video_num)
c_CQM = c_CQM / float(video_num)
#save the category evaluation results
with open(result_file, 'a+') as fid:
fid.write('\r\n' + category[0:min(8, len(category))] +
'_AVG::\t\t' + str(round(c_AGE, 4)) + '\t' +
str(round(c_pEPs, 4)) + '\t' + str(round(c_pCEPs, 4)) +
'\t' + str(round(c_MSSSIM, 4)) + '\t' +
str(round(c_PSNR, 4)) + '\t' + str(round(c_CQM, 4)) +
'\r\n\r\n')
m_AGE = m_AGE + c_AGE
m_pEPs = m_pEPs + c_pEPs
m_pCEPs = m_pCEPs + c_pCEPs
m_MSSSIM = m_MSSSIM + c_MSSSIM
m_PSNR = m_PSNR + c_PSNR
m_CQM = m_CQM + c_CQM
#save the method evaluation results
m_AGE = m_AGE / float(category_num)
m_pEPs = m_pEPs / float(category_num)
m_pCEPs = m_pCEPs / float(category_num)
m_MSSSIM = m_MSSSIM / float(category_num)
m_PSNR = m_PSNR / float(category_num)
m_CQM = m_CQM / float(category_num)
with open(result_file, 'a+') as fid:
fid.write('Total:\t\t\t' + str(round(m_AGE, 4)) + '\t' +
str(round(m_pEPs, 4)) + '\t' + str(round(m_pCEPs, 4)) +
'\t' + str(round(m_MSSSIM, 4)) + '\t' +
str(round(m_PSNR, 4)) + '\t' + str(round(m_CQM, 4)) + '\r\n')
