def run_differ_C(args):
algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt']
config_dict = {
name: Config(os.path.join(relative_path_root, 'config',
name + '.conf'))
for name in algorithm_list
}
conf_path = os.path.join(relative_path_root, 'config',
'account_{}.conf'.format(args.dataset))
print('Account configuration path: {}'.format(conf_path))
conf_account = Config(conf_path)
conf_account['output.setup'] = os.path.join(
os.path.dirname(os.path.dirname(conf_account['output.setup'])),
'varies_C/')
account_data = AccountDAO(conf_account)
for name, config in config_dict.items():
if name.startswith('ABPR'):
config.update_inferior(conf_account)
else:
config.update(conf_account)
for C in range(1, 11):
recSys_all = RecQMultiAlgo(config_dict,
conf_account,
account_data,
C=C)
# args.debug = True
recSys_all.execute_all(debug=args.debug)
def __init__(self, conf):
self.config = Config(conf)
self.userProfile = FileIO.loadDataSet(self.config,
self.config['ratings'])
self.itemProfile = defaultdict(dict)
self.attackSize = float(self.config['attackSize'])
self.fillerSize = float(self.config['fillerSize'])
self.selectedSize = float(self.config['selectedSize'])
self.targetCount = int(self.config['targetCount'])
self.targetScore = float(self.config['targetScore'])
self.threshold = float(self.config['threshold'])
self.minCount = int(self.config['minCount'])
self.maxCount = int(self.config['maxCount'])
self.outputDir = self.config['outputDir']
if not os.path.exists(self.outputDir):
os.makedirs(self.outputDir)
for user in self.userProfile:
for item in self.userProfile[user]:
self.itemProfile[item][user] = self.userProfile[user][item]
self.spamProfile = defaultdict(dict)
self.spamItem = defaultdict(list) #items rated by spammers
self.targetItems = []
self.itemAverage = {}
self.getAverageRating()
self.selectTarget()
def run_differ_N(args):
algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1']
config_dict = {
name: Config(os.path.join(relative_path_root, 'config',
name + '.conf'))
for name in algorithm_list
}
conf_path = os.path.join(relative_path_root, 'config',
'account_{}.conf'.format(args.dataset))
print('Account configuration path: {}'.format(conf_path))
conf_account = Config(conf_path)
dirs = walk_dir('../account_data/lastfm-dataset-1K/differ_N')
conf_account['account.data'] = os.path.join(
os.path.dirname(conf_account['account.data']), 'differ_N/',
os.path.basename(conf_account['account.data']))
conf_account['output.setup'] = os.path.join(
os.path.dirname(os.path.dirname(conf_account['output.setup'])),
'varies_N/')
if args.dataset == 'movie':
conf_account['account.algorithm'] = 'PDAN'
for dir in dirs:
print("dir is {}".format(dir))
conf_account['account.data'] = os.path.join(
os.path.dirname(conf_account['account.data']), dir)
# conf_account['output.setup'] = os.path.join(conf_account['output.setup'], dir)
account_data = AccountDAO(conf_account)
for name, config in config_dict.items():
if name.startswith('ABPR'):
config.update_inferior(conf_account)
else:
config.update(conf_account)
recSys_all = RecQMultiAlgo(config_dict,
conf_account,
account_data,
N=int(dir[1:]))
args.debug = True
recSys_all.execute_all(debug=args.debug)
def recommend(self, i, id, location, num):
print(id + "\t" + num + "\t" + i + '\t' +
urllib.unquote(location).decode('utf-8'))
location = urllib.unquote(location).decode('utf-8')
conf = Config('../config/UserKNN.conf')
if num is None or num < 3:
num = 3
if int(i) == 1:
sysRec = RecQ(conf)
sysRec.execute()
model_f = open(conf['model.file'])
lr = []
for line in model_f:
l = line.split(' ')
if str(l[0]) == str(id):
lr.append(line)
print("l:{}".format(l))
l = []
for r in lr:
rs = str(r).split(' ')
if len(l) < int(num):
l.append(rs[1])
print("r :{}".format(rs[1]))
s = []
lt = []
with open(conf['stock.file']) as f:
for ind, line in enumerate(f):
if line.strip() <> '':
li = line.split("\t")
x = li[0]
e = li[1]
loc = li[2]
if len(l) == 0:
s.append(str(x))
else:
b_append = False
for k in l:
if e == k:
lt.append(str(x))
elif location == loc.decode('utf-8'):
b_append = True
if b_append:
s.append(str(x))
le = len(s)
print(le)
if le > (int(num) - len(l)):
ret = random.sample(s, int(num) - len(l))
for r in ret:
lt.append(r)
else:
for r in s:
lt.append(r)
return {"list": [lt], "code": 200}
def run_differ_L(args):
# algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1', 'BPR', 'WRMF', 'ExpoMF', 'CoFactor',
# 'NeuMF', 'APR', 'CDAE', 'CFGAN']
# algorithm_list = ['NeuMF', 'APR', 'CDAE', 'CFGAN']
algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1']
config_dict = {
name: Config(os.path.join(relative_path_root, 'config',
name + '.conf'))
for name in algorithm_list
}
conf_path = os.path.join(relative_path_root, 'config',
'account_{}.conf'.format(args.dataset))
print('Account configuration path: {}'.format(conf_path))
conf_account = Config(conf_path)
conf_account['output.setup'] = os.path.join(
os.path.dirname(os.path.dirname(conf_account['output.setup'])),
'varies_L/')
account_data = AccountDAO(conf_account)
for name, config in config_dict.items():
if name.startswith('ABPR'):
config.update_inferior(conf_account)
else:
config.update(conf_account)
for L in range(0, 10):
recSys_all = RecQMultiAlgo(config_dict,
conf_account,
account_data,
L=L)
# args.debug = True
recSys_all.execute_all(debug=args.debug)
print 'Baselines:'
print 'b1. UserMean b2. ItemMean b3. MostPopular b4. Rand'
print '=' * 80
algor = -1
conf = -1
order = raw_input('please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '0':
try:
import seaborn as sns
except ImportError:
print '!!!To obtain nice data charts, ' \
'we strongly recommend you to install the third-party package <seaborn>!!!'
conf = Config('../config/visual/visual.conf')
Display(conf).render()
exit(0)
elif order == '1':
conf = Config('../config/UserKNN.conf')
elif order == '2':
conf = Config('../config/ItemKNN.conf')
elif order == '3':
conf = Config('../config/BasicMF.conf')
elif order == '4':
conf = Config('../config/SlopeOne.conf')
elif order == '5':
116.280,
103.530,
]
path = '../data_preprocess/'
Bboxes = [path + 'Bboxes_07.pkl', path + 'Bboxes_12.pkl']
img_paths = [path + 'img_paths_07.pkl', path + 'img_paths_12.pkl']
files = [img_paths, Bboxes]
config = Config(True,
Mean,
files,
lr=0.001,
weight_decay=0.0005,
batch_size_per_GPU=1,
img_max=1000,
img_min=600,
roi_min_size=16,
roi_train_pre_nms=12000,
roi_train_post_nms=2000,
roi_test_pre_nms=6000,
roi_test_post_nms=300,
bias_lr_factor=2,
multi_scale_train=False)
step = 0
model = Faster_Rcnn
x = 1
pre_model_file = r'D:/BaiduNetdiskDownload/vgg16_cf.pth'
pre_model_file = '/home/zhai/PycharmProjects/Demo35/py_Faster_tool/pre_model/vgg16_cf.pth'
model_file = r''
train(model, config, step, x, pre_model_file, model_file=model_file)
print 'Advanced Recommenders:'
print 'a1. MEM'
print 'Baselines:'
print 'b1. MostPop b2. Rand'
print '='*80
algor = -1
conf = -1
order = raw_input('Please enter the num of the algorithm to run it:')
import time
s = time.time()
if order=='1':
conf = Config('./config/BPR.conf')
elif order=='2':
conf = Config('./config/FISM.conf')
elif order == 'b1':
conf = Config('./config/MostPop.conf')
elif order == 'b2':
conf = Config('./config/rand.conf')
elif order == 'a1':
conf = Config('./config/MEM.conf')
else:
print 'Error num!'
algor = -1
conf = -1
order = input('please enter the num of the method to run it:')
import time
s = time.time()
# if order == 0:
# try:
# import seaborn as sns
# except ImportError:
# print '!!!To obtain nice data charts, ' \
# 'we strongly recommend you to install the third-party package <seaborn>!!!'
# conf = Config('../config/visual/visual.conf')
# Display(conf).render()
# exit(0)
if order == 1:
conf = Config('../config/DegreeSAD.conf')
elif order == 2:
conf = Config('../config/CoDetector.conf')
elif order == 3:
conf = Config('../config/SemiSAD.conf')
elif order == 4:
conf = Config('../config/PCASelectUsers.conf')
elif order == 5:
conf = Config('../config/FAP.conf')
else:
print 'Error num!'
with tf.Session(config=config) as sess:
self.init(sess, self.Name, tf.global_variables(), W)
constant_graph = graph_util.convert_variables_to_constants(
sess, sess.graph_def, self.out)
with tf.gfile.FastGFile('./models/YOLOv3_tiny_3l_20_run_model.pb',
mode='wb') as f:
f.write(constant_graph.SerializeToString())
print('finish')
if __name__ == "__main__":
files = None
cfg_file = r'D:\PycharmProjects\daknet_demo\train\brand_20_classes_train\cfg\yolov3-tiny_3l_brand_20.cfg'
weights_file = r'D:\PycharmProjects\daknet_demo\train\brand_20_classes_train\backup\yolov3-tiny_3l_brand_20_25000.weights'
config = Config(files,
cfg_file,
weights_file,
num_cls=20,
img_size=(416, 416),
ignore_thresh=0.5,
stride=[32, 16, 8],
equal_scale=False)
yolov3 = YOLOv3(config)
# yolov3.test()
# yolov3.test_coco()
# yolov3.test_tiny()
# yolov3.test_vedio()
yolov3.save_pb()
print '0. Analyze the input data.(Configure the visual.conf in config/visual first.)'
print '1. UserKNN 2. ItemKNN 3. BasicMF 4. SlopeOne 5. RSTE 6. UserMean'
print '7. ItemMean 8. SVD 9. PMF 10. TrustMF 11. SocialMF 12. SoRec 13.SoReg 14.SVD++'
algor = -1
conf = -1
print '-' * 80
order = input('please enter the num of the algorithm to run it:')
import time
s = time.clock()
if order == 0:
try:
import seaborn as sns
except ImportError:
print '!!!To obtain nice data charts, ' \
'we strongly recommend you to install the third-party package <seaborn>!!!'
conf = Config('../config/visual/visual.conf')
Display(conf).render()
exit(0)
elif order == 1:
conf = Config('../config/UserKNN.conf')
elif order == 2:
conf = Config('../config/ItemKNN.conf')
elif order == 3:
conf = Config('../config/BasicMF.conf')
elif order == 4:
conf = Config('../config/SlopeOne.conf')
elif order == 5:
print('Baselines:')
print('b1. UserMean b2. ItemMean b3. MostPopular b4. Rand')
print('=' * 80)
algor = -1
conf = -1
order = input('please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '0':
try:
import seaborn as sns
except ImportError:
print('!!!To obtain nice data charts, ' \
'we strongly recommend you to install the third-party package <seaborn>!!!')
conf = Config('../config/visual/visual.conf')
Display(conf).render()
exit(0)
algorthms = {
'1': 'UserKNN',
'2': 'ItemKNN',
'3': 'BasicMF',
'4': 'SlopeOne',
'5': 'SVD',
'6': 'PMF',
'7': 'SVD++',
'8': 'EE',
'9': 'BPR',
'10': 'WRMF',
'11': 'ExpoMF',
0: 'batch'
},
'out2': {
0: 'batch'
}
})
if __name__ == "__main__":
font = cv2.FONT_HERSHEY_SIMPLEX
files = None
cfg_file = r'yolo-fastest-1.1_body/yolo-fastest-1.1_body.cfg'
weights_file = r'yolo-fastest-1.1_body/yolo-fastest-1.1_body.weights'
config = Config(files, cfg_file, weights_file, equal_scale=False)
model = YOLOv3(config)
print(model)
model.eval()
# model.eval()
model_static = model.state_dict()
print(len(model_static))
vars_shapes = []
keys = []
for key in model_static.keys():
if 'num_batches_tracked' in key:
continue
v = model_static[key]
vars_shapes.append(v.shape)
keys.append(key)
Mean = [123.68, 116.78, 103.94]
Mean = [
123.675,
116.280,
103.530,
]
config = Config(
False,
Mean,
None,
lr=0.00125,
weight_decay=0.0001,
num_cls=80,
img_max=1333,
img_min=800,
anchor_scales=[[32], [64], [128], [256], [512]],
anchor_ratios=[[0.5, 1, 2], [0.5, 1, 2], [0.5, 1, 2], [0.5, 1, 2],
[0.5, 1, 2]],
fast_n_sample=512,
roi_min_size=[1, 1, 1, 1, 1],
roi_train_pre_nms=12000,
roi_train_post_nms=2000,
roi_test_pre_nms=6000,
roi_test_post_nms=1000,
)
model = Mask_Rcnn
model_file = '../train_one_GPU/models/Mask_Rcnn_cascade_50_90000_1.pth'
test(model, config, model_file)
# Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.254
+ '\t' + gender + '\t' + action
except:
print("error:" + str(line))
@staticmethod
def read_mapper_output(file, separator='\t'):
for line in file:
l = line.rstrip().split(separator)
if len(l) > 5:
user = l[0]
item = l[1]
weight = l[6]
yield (user + "|" + item, weight)
def reduce(self):
rate_f = file(self.config['ratings'], 'w')
data = self.read_mapper_output(open(self.config['process.log.file']))
for user, group in groupby(data, itemgetter(0)):
total_count = sum(int(count) for item, count in group)
# print("%s%s%d" % (user, '\t', total_count))
u = user.split('|')
print >> rate_f, u[1], u[0], total_count
if __name__ == '__main__':
# clean()
# reduce()
config = Config('../config/UserKNN.conf')
p = process(config)
p.clean()
print('==========', datetime.now() - start_time)
joblib.dump(Res, 'Faster_vgg16_1.pkl')
GT = joblib.load('../mAP/voc_GT.pkl')
AP = mAP(Res, GT, 20, iou_thresh=0.5, use_07_metric=True, e=0.05)
print(AP)
AP = AP.mean()
print(AP)
if __name__ == "__main__":
Mean = [123.68, 116.78, 103.94]
Mean = [
123.675,
116.280,
103.530,
]
model = Faster_Rcnn
config = Config(False,
Mean,
None,
img_max=1000,
img_min=600,
roi_min_size=16)
model_file = '../train_one_GPU/models/vgg16_90000_2.pth'
test(model, config, model_file)
# [0.73909475 0.78086676 0.7028246 0.60206878 0.61172184 0.82720654
# 0.83681608 0.85766458 0.55384811 0.77337219 0.63840307 0.83138268
# 0.84698006 0.76642416 0.7870849 0.44357723 0.75472496 0.67486161
# 0.81124731 0.74521005]
# 0.7292690138425238
cv2.imshow('a', im)
cv2.waitKey(2000)
return im
if __name__ == "__main__":
from datetime import datetime
from tool.config import Config
Mean = [123.68, 116.78, 103.94]
path = '/home/zhai/PycharmProjects/Demo35/pytorch_Faster_tool/data_preprocess/'
Bboxes = [path + 'coco_bboxes_2017.pkl']
img_paths = [path + 'coco_imgpaths_2017']
masks = [path + 'coco_mask_2017.pkl']
files = [img_paths, Bboxes, masks]
config = Config(True, Mean, files, lr=0.00125, img_max=1000, img_min=600)
dataset = Read_Data(config)
dataloader = DataLoader(dataset, batch_size=1, num_workers=16, collate_fn=lambda x: x)
dataloader = DataLoader(dataset, batch_size=2, collate_fn=func,
shuffle=True, drop_last=True, pin_memory=False, num_workers=1)
c = 0
for i in range(2):
for imgs, bboxes, num_b, num_H, num_W, masks in dataloader:
# imgs, bboxes, num_b, num_H, num_W, masks = x
c += 1
print(datetime.now(), c, masks.shape, imgs.shape)
# print(img.shape, bboxes.shape, masks.shape)
for j in range(imgs.shape[0]):
x = imgs[j]
H = num_H[j]
import sys
sys.path.append("..")
from tool.config import Config
from tool.file import FileIO
from algorithm.SoReg import SoReg
if __name__ == '__main__':
print'='*80
print'this is the algorithm of the test'
algor = -1
conf = -1
conf = Config('../conf/SoReg.conf')
trainset = []
testset = []
relation = []
ui={}
vj={}
user_item_avg={}
trainset = FileIO.loadDataSet(conf,conf['ratings'],bTest=False)
testset = FileIO.loadDataSet(conf,conf['testset'],bTest=True)
relation = FileIO.loadRelationship(conf,conf['social'])
ui,vj,user_item_avg=SoReg.buildmodel(trainset,relation)
SoReg.pred(testset,ui,vj,user_item_avg)
#sotre the data
print 'Hybrid Recommenders:\n'
print 'Advanced Recommenders:'
print 'a1. MEM a2. SocialMR a3. HME a4. CUNE'
print 'a5. Song2vec'
print 'Baselines:'
print 'b1. MostPop b2. Rand'
print '=' * 80
algor = -1
conf = -1
order = raw_input('Please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '1':
conf = Config('./config/BPR.conf')
elif order == '2':
conf = Config('./config/FISM.conf')
elif order == '3':
conf = Config('./config/WRMF.conf')
elif order == '4':
conf = Config('./config/IPF.conf')
elif order == '5':
conf = Config('./config/UserKNN.conf')
elif order == 'b1':
conf = Config('./config/MostPop.conf')
from algorithm.rating.UserKNN import UserKNN
from algorithm.rating.ItemKNN import ItemKNN
from tool.config import Config
if __name__ == '__main__':
print '=' * 80
print ' RecQ: An effective python-based recommender algorithm library. '
print '=' * 80
print '1. UserKNN 2. ItemKNN'
algor = 0
order = input('please enter the num of the algorithm to run it:')
import time
s = time.clock()
if order == 1:
conf = Config('../config/UserKNN')
algor = UserKNN(conf)
elif order == 2:
conf = Config('../config/itemKNN')
algor = ItemKNN(conf)
else:
print 'Error num!'
exit()
algor.execute()
e = time.clock()
print "Run time: %f s" % (e - s)
print 'Baselines:'
print 'b1. UserMean b2. ItemMean'
print '=' * 80
algor = -1
conf = -1
order = raw_input('please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '0':
try:
import seaborn as sns
except ImportError:
print '!!!To obtain nice data charts, ' \
'we strongly recommend you to install the third-party package <seaborn>!!!'
conf = Config('../config/visual/visual.conf')
Display(conf).render()
exit(0)
elif order == '1':
conf = Config('../config/UserKNN.conf')
elif order == '2':
conf = Config('../config/ItemKNN.conf')
elif order == '3':
conf = Config('../config/BasicMF.conf')
elif order == '4':
conf = Config('../config/SlopeOne.conf')
elif order == '5':
if (i) % 1000 == 0:
print(datetime.now(), c, i, c / i)
print(c)
print(c / m)
a = [chr(ord('0') + i) for i in range(10)]
b = [chr(ord('a') + i) for i in range(26)]
c = [chr(ord('A') + i) for i in range(26)]
chars = a + b + c
chars_dic = {}
for i in range(len(chars)):
chars_dic[i] = chars[i]
chars_dic[chars[i]] = i
if __name__ == "__main__":
Mean = np.array([123.68, 116.78, 103.94], dtype=np.float32)
path = '/home/zhai/PycharmProjects/Demo35/CRNN/'
# files = [path + 'voc_07.tf', path + 'voc_12.tf']
files = [path + 'mnt.tf']
# files=[path+'coco_train2017_scale.tf']
config = Config(True, Mean, files, num_cls=62, lr=0.01, batch_size_per_GPU=64, gpus=1)
crnn = CRNN(config)
crnn.test()
# 1x
# 0.8914260920456495
# 2x
# 0.9035560085074227
algorithm_list = ['DMF', 'CFGAN']
algorithm_list = ['ABPR', 'BPR', 'WRMF', 'ExpoMF', 'CoFactor', 'CUNE_BPR']
algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1', 'BPR', 'WRMF', 'ExpoMF', 'CoFactor', 'CUNE_BPR']
algorithm_list = ['NeuMF', 'APR', 'CDAE', 'DMF', 'CFGAN']
algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1', 'BPR', 'WRMF', 'ExpoMF', 'CoFactor',
'NeuMF', 'APR', 'CDAE', 'CFGAN']
# algorithm_list = ['ABPR', 'ABPR_10', 'ABPR_d', 'ABPR_sqrt', 'ABPR_t1', 'BPR', 'WRMF', 'ExpoMF', 'CoFactor',
# 'CUNE_BPR']
# algorithm_list = ['NeuMF', 'APR', 'CDAE']
# algorithm_list = ['ABPR', 'ABPR_sqrt']
config_dict = {name: Config(os.path.join(relative_path_root, 'config', name + '.conf')) for name in algorithm_list}
conf_path = os.path.join(relative_path_root, 'config', 'account_{}.conf'.format(args.dataset))
print('Account configuration path: {}'.format(conf_path))
conf_account = Config(conf_path)
account_data = AccountDAO(conf_account)
for name, config in config_dict.items():
if name.startswith('ABPR'):
config.update_inferior(conf_account)
else:
config.update(conf_account)
recSys_all = RecQMultiAlgo(config_dict, conf_account, account_data)
# args.debug = True
print ('a4. CDAE a5. DMF')
s = time.time()
print ('Baselines:')
print ('b1. MostPop b2. Rand')
print ('='*80)
algor = -1
conf = -1
order = input('Please enter the num of the algorithm to run it:')
import time
s = time.time()
if order=='1':
conf = Config('./config/BPR.conf')
elif order=='2':
conf = Config('./config/FISM.conf')
elif order=='3':
conf = Config('./config/WRMF.conf')
elif order=='4':
conf = Config('./config/IPF.conf')
elif order=='5':
conf = Config('./config/UserKNN.conf')
elif order == 'b1':
conf = Config('./config/MostPop.conf')
path = r'../data_preprocess/'
Bboxes = [path + 'coco_bboxes_2017.pkl']
img_paths = [path + 'coco_imgpaths_2017.pkl']
masks = [path + 'coco_mask_2017.pkl']
files = [img_paths, Bboxes, masks]
config = Config(True,
Mean,
files,
num_cls=80,
lr=0.00125,
weight_decay=0.0001,
batch_size_per_GPU=1,
img_max=1333,
img_min=800,
anchor_scales=[[32], [64], [128], [256], [512]],
anchor_ratios=[[0.5, 1, 2], [0.5, 1, 2], [0.5, 1, 2],
[0.5, 1, 2], [0.5, 1, 2]],
fast_n_sample=512,
bias_lr_factor=2,
roi_min_size=[1, 1, 1, 1, 1],
roi_train_pre_nms=12000,
roi_train_post_nms=2000,
roi_test_pre_nms=6000,
roi_test_post_nms=1000,
multi_scale_train=True)
step = 0
model = Mask_Rcnn
x = 1
pre_model_file = r'D:\BaiduNetdiskDownload\pytorch_resnet_caffe/resnet50-caffe.pth'
model_file = r''
train(model, config, step, x, pre_model_file, model_file=model_file)
from algorithm.rating.SVD import SVD
from tool.config import Config
if __name__ == '__main__':
print '=' * 80
print ' RecQ: An effective python-based recommender algorithm library. '
print '=' * 80
print '1. UserKNN 2. ItemKNN 3. BasicMF 4. SlopeOne 5. RSTE 6. UserMean'
print '7. ItemMean 8. SVD'
algor = 0
order = input('please enter the num of the algorithm to run it:')
import time
s = time.clock()
if order == 1:
conf = Config('../config/UserKNN.conf')
algor = UserKNN(conf)
elif order == 2:
conf = Config('../config/ItemKNN.conf')
algor = ItemKNN(conf)
elif order == 3:
conf = Config('../config/BasicMF.conf')
algor = BasicMF(conf)
elif order == 4:
conf = Config('../config/SlopeOne.conf')
algor = SlopeOne(conf)
elif order == 5:
conf = Config('../config/RSTE.conf')
algor = RSTE(conf)
elif order == 6:
conf = Config('../config/UserMean.conf')
order = 6 # input('please enter the num of the method to run it:')
import time
s = time.clock()
# if order == 0:
# try:
# import seaborn as sns
# except ImportError:
# print '!!!To obtain nice data charts, ' \
# 'we strongly recommend you to install the third-party package <seaborn>!!!'
# conf = Config('../config/visual/visual.conf')
# Display(conf).render()
# exit(0)
if order == 1:
conf = Config('../config/DegreeSAD_tmp.conf')
elif order == 2:
conf = Config('../config/CoDetector.conf')
elif order == 3:
conf = Config('../config/BayesDetector.conf')
elif order == 4:
conf = Config('../config/SemiSAD.conf')
elif order == 5:
conf = Config('../config/PCASelectUsers.conf')
elif order == 6:
conf = Config('../config/FAP.conf')
import time
s = time.time()
# if order == '0':
# try:
# import seaborn as sns
# except ImportError:
# print '!!!To obtain nice data charts, ' \
# 'we strongly recommend you to install the third-party package <seaborn>!!!'
# conf = Config('../config/visual/visual.conf')
# Display(conf).render()
# exit(0)
algorthms = {'1':'UserKNN','2':'ItemKNN','3':'BasicMF','4':'SlopeOne','5':'SVD','6':'PMF',
'7':'SVD++','8':'EE','9':'BPR','10':'WRMF','11':'ExpoMF',
's1':'RSTE','s2':'SoRec','s3':'SoReg','s4':'SocialMF','s5':'SBPR','s6':'SREE',
's7':'LOCABAL','s8':'SocialFD','s9':'TBPR','s10':'SEREC','a1':'CoFactor',
'a2':'CUNE_MF','a3':'CUNE_BPR','a4':'IF_BPR',
'd1':'APR','d2':'CDAE','d3':'DMF','d4':'NeuMF','d5':'CFGAN','d6':'IRGAN','d7':'RSGAN','d8':'NGCF',
'd9':'LightGCN', 'd10':'ESRF', 'd11':'DHCF', 'd12':'DiffNet','d13':'MHCN',
'b1':'UserMean','b2':'ItemMean','b3':'MostPopular','b4':'Rand'}
try:
conf = Config('../config/'+algorthms[order]+'.conf')
except KeyError:
print 'Error num!'
exit(-1)
recSys = RecQ(conf)
recSys.execute()
e = time.time()
print "Run time: %f s" % (e - s)
print 'Baselines:'
print 'b1. UserMean b2. ItemMean b3. MostPopular b4. Rand'
print '=' * 80
algor = -1
conf = -1
order = raw_input('please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '0':
try:
import seaborn as sns
except ImportError:
print '!!!To obtain nice data charts, ' \
'we strongly recommend you to install the third-party package <seaborn>!!!'
conf = Config('../config/visual/visual.conf')
Display(conf).render()
exit(0)
elif order == '1':
conf = Config('../config/UserKNN.conf')
elif order == '2':
conf = Config('../config/ItemKNN.conf')
elif order == '3':
conf = Config('../config/BasicMF.conf')
elif order == '4':
conf = Config('../config/SlopeOne.conf')
elif order == '5':
print 'Hybrid Recommenders:\n'
print 'Advanced Recommenders:'
print 'a1. MEM a2. SocialMR a3. HME'
print 'Baselines:'
print 'b1. MostPop b2. Rand'
print '=' * 80
algor = -1
conf = -1
order = raw_input('Please enter the num of the algorithm to run it:')
import time
s = time.time()
if order == '1':
conf = Config('./config/BPR.conf')
elif order == '2':
conf = Config('./config/FISM.conf')
elif order == '3':
conf = Config('./config/WRMF.conf')
elif order == 'b1':
conf = Config('./config/MostPop.conf')
elif order == 'b2':
conf = Config('./config/rand.conf')
elif order == 'a1':
conf = Config('./config/MEM.conf')
