def Supervised(filename, old_files = [], stop='', stopat=1, error='none', interval = 100000, starting =1, seed=0,
step =10, learner='svm_linear', boost=None):
print("FILENAME: ", filename, "OLDFILES: ", len(old_files))
stopat = float(stopat)
np.random.seed(seed)
read = MAR()
read = read.create(filename, old_files)
read.step = step
read.interval = interval
read.seed = seed
if boost:
util.vote(read, clf_name=boost, seed=seed, all=False, temp=str(seed) + filename)
return
num2 = read.get_allpos()
target = int(num2 * stopat)
if stop == 'est':
read.enable_est = True
else:
read.enable_est = False
if boost == None:
read.train_supervised(learner, seed)
pos, neg, total = read.get_numbers()
if boost:
read.query_boost()
else:
read.query_supervised()
read.record['est'][0] = read.est_num
while True:
pos, neg, total = read.get_numbers()
# try:
# print("%d, %d, %d" %(pos,pos+neg, read.est_num))
# except:
# print("%d, %d" %(pos,pos+neg))
if pos + neg >= total:
break
# if pos >= target and (pos+neg) >= total * .22 and read.enable_est and read.est_num*stopat<= pos:
# break
if boost:
ids = read.query_boost()[:read.step]
else:
ids = read.query_supervised()[:read.step]
read.code_batch(ids)
return read
def TEST_AL(filename,
old_files=[],
stop='est',
stopat=1,
error='none',
interval=100000,
starting=1,
seed=0,
step=10):
stopat = float(stopat)
thres = 0
counter = 0
pos_last = 0
np.random.seed(seed)
read = MAR()
read = read.create(filename, old_files)
read.step = step
read.interval = interval
num2 = read.get_allpos()
target = int(num2 * stopat)
if stop == 'est':
read.enable_est = True
else:
read.enable_est = False
while True:
pos, neg, total = read.get_numbers()
try:
print("%d, %d, %d" % (pos, pos + neg, read.est_num))
except:
print("%d, %d" % (pos, pos + neg))
if pos + neg >= total:
break
if pos < starting or pos + neg < thres:
for id in read.random():
read.code_error(id, error=error)
else:
a, b, c, d = read.train(weighting=True, pne=True)
if pos >= target and read.est_num * stopat <= pos:
break
for id in c:
read.code_error(id, error=error)
# read.export()
# results = analyze(read)
# print(results)
# read.plot()
return read
def Supervised(filename,
old_files=[],
stop='est',
stopat=1,
error='none',
interval=100000,
starting=1,
seed=0,
step=10):
stopat = float(stopat)
np.random.seed(seed)
read = MAR()
read = read.create(filename, old_files)
read.step = step
read.interval = interval
num2 = read.get_allpos()
target = int(num2 * stopat)
if stop == 'est':
read.enable_est = True
else:
read.enable_est = False
read.train_supervised()
pos, neg, total = read.get_numbers()
read.query_supervised()
read.record['est'][0] = read.est_num
while True:
pos, neg, total = read.get_numbers()
try:
print("%d, %d, %d" % (pos, pos + neg, read.est_num))
except:
print("%d, %d" % (pos, pos + neg))
if pos + neg >= total:
break
if pos >= target and read.est_num * stopat <= pos:
break
for id in read.query_supervised()[:read.step]:
read.code_error(id, error=error)
return read
def Boosting(filename, old_files = [], stop='', stopat=1, error='none', interval = 100000, starting =1, seed=0, step =10):
print("FILENAME: ", filename, "OLDFILES: ", len(old_files))
stopat = float(stopat)
np.random.seed(seed)
read = MAR()
read = read.create(filename,old_files)
read.step = step
read.interval = interval
util.vote(read)
num2 = read.get_allpos()
target = int(num2 * stopat)
if stop == 'est':
read.enable_est = True
else:
read.enable_est = False
pos, neg, total = read.get_numbers()
read.query_boost()
read.record['est'][0]= read.est_num
while True:
pos, neg, total = read.get_numbers()
try:
print("%d, %d, %d" %(pos,pos+neg, read.est_num))
except:
print("%d, %d" %(pos,pos+neg))
if pos + neg >= total:
break
if read.enable_est and read.est_num*stopat<= pos:
break
for id in read.query_boost()[:read.step]:
read.code_error(id, error=error)
return read