bug fix, trainer
This commit is contained in:
Steve Nyemba
parent
d72fb6b4e3
commit
718e578401
|
|
@ -581,7 +581,6 @@ class Predict(GNet):
|
|||
|
||||
df = pd.DataFrame(np.round(f)).astype(np.int32)
|
||||
|
||||
|
||||
p = 0 not in df.sum(axis=1).values
|
||||
x = df.sum(axis=1).values
|
||||
|
||||
|
|
@ -599,7 +598,8 @@ class Predict(GNet):
|
|||
#
|
||||
# In case we are dealing with actual values like diagnosis codes we can perform
|
||||
#
|
||||
_index = [found.index(item) for item in found if item.shape[1] == len(self.values)]
|
||||
N = len(found)
|
||||
_index = [i for i in range(0,N) if found[i].shape[1] == len(self.values)]
|
||||
if not _index :
|
||||
INDEX = np.random.choice(np.arange(len(found)),1)[0]
|
||||
INDEX = ratio.index(np.max(ratio))
|
||||
|
|
|
|||
|
|
@ -37,11 +37,14 @@ class ContinuousToDiscrete :
|
|||
index = BOUNDS.index(row)
|
||||
x_[index] = 1
|
||||
break
|
||||
|
||||
#
|
||||
# for items in BOUNDS :
|
||||
# index = BOUNDS.index(items)
|
||||
return _matrix
|
||||
|
||||
@staticmethod
|
||||
def bounds(x,n):
|
||||
# return np.array_split(x,n)
|
||||
return list(pd.cut(np.array( np.round(x,ContinuousToDiscrete.ROUND_UP) ),n).categories)
|
||||
|
||||
|
||||
|
|
@ -175,7 +178,8 @@ def generate(**args):
|
|||
handler.load_meta(col)
|
||||
r = handler.apply()
|
||||
BIN_SIZE = 4 if 'bin_size' not in args else int(args['bin_size'])
|
||||
_df[col] = ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if 'float' in df[col].dtypes.name or col in CONTINUOUS else r[col]
|
||||
_df[col] = ContinuousToDiscrete.continuous(r[col],BIN_SIZE) if col in CONTINUOUS else r[col]
|
||||
# _df[col] = r[col]
|
||||
#
|
||||
# @TODO: log basic stats about the synthetic attribute
|
||||
#
|
||||
|
|
|
|||
60
pipeline.py
60
pipeline.py
|
|
@ -50,11 +50,12 @@ class Components :
|
|||
"""
|
||||
#
|
||||
# @TODO: we need to log something here about the parameters being passed
|
||||
pointer = args['reader'] if 'reader' in args else lambda: Components.get(**args)
|
||||
df = pointer()
|
||||
if df.shape[0] == 0 :
|
||||
print ("CAN NOT TRAIN EMPTY DATASET ")
|
||||
return
|
||||
# pointer = args['reader'] if 'reader' in args else lambda: Components.get(**args)
|
||||
df = args['reader']()
|
||||
|
||||
# if df.shape[0] == 0 :
|
||||
# print ("CAN NOT TRAIN EMPTY DATASET ")
|
||||
# return
|
||||
#
|
||||
# Now we can parse the arguments and submit the entire thing to training
|
||||
#
|
||||
|
|
@ -113,18 +114,29 @@ class Components :
|
|||
|
||||
pass
|
||||
else:
|
||||
print ('.....')
|
||||
partition = args['partition'] if 'partition' in args else ''
|
||||
log_folder = os.sep.join([log_folder,args['context'],partition])
|
||||
log_folder = os.sep.join([log_folder,args['context'],str(partition)])
|
||||
_args = {"batch_size":10000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
|
||||
_args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
|
||||
_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
|
||||
|
||||
#
|
||||
# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
|
||||
#
|
||||
if int(args['num_gpu']) > 1 :
|
||||
_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)[0]
|
||||
else:
|
||||
_args['gpu'] = 0
|
||||
_args['num_gpu'] = 1
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu'])
|
||||
|
||||
|
||||
_args['data'] = df
|
||||
#
|
||||
# @log :
|
||||
# Logging information about the training process for this partition (or not)
|
||||
#
|
||||
|
||||
info = {"rows":df.shape[0],"cols":df.shape[1], "partition":int(partition),"logs":_args['logs']}
|
||||
|
||||
logger.write({"module":"train","action":"train","input":info})
|
||||
|
|
@ -291,7 +303,7 @@ if __name__ == '__main__' :
|
|||
if ''.join(content).isnumeric() :
|
||||
#
|
||||
# we have partitions we are working with
|
||||
make = lambda _args: (Components()).generate(_args)
|
||||
|
||||
jobs = []
|
||||
del args['reader']
|
||||
columns = DATA.columns.tolist()
|
||||
|
|
@ -310,13 +322,13 @@ if __name__ == '__main__' :
|
|||
args['gpu'] = id
|
||||
else:
|
||||
args['gpu']=0
|
||||
|
||||
make = lambda _args: (Components()).generate(_args)
|
||||
job = Process(target=make,args=(args,))
|
||||
job.name = 'generator # '+str(id)
|
||||
job.start()
|
||||
jobs.append(job)
|
||||
|
||||
print (["Started ",len(jobs),"generator"+"s" if len(jobs)>1 else "" ])
|
||||
print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
|
||||
while len(jobs)> 0 :
|
||||
jobs = [job for job in jobs if job.is_alive()]
|
||||
time.sleep(2)
|
||||
|
|
@ -358,9 +370,31 @@ if __name__ == '__main__' :
|
|||
# qreader.read(1)
|
||||
pass
|
||||
else:
|
||||
PART_SIZE = int(args['jobs']) if 'jobs' in args else 8
|
||||
DATA = reader()
|
||||
DATA = np.array_split(DATA[args['columns']],PART_SIZE)
|
||||
jobs = []
|
||||
for index in range(0,int(args['jobs'])) :
|
||||
if 'focus' in args and int(args['focus']) != index :
|
||||
continue
|
||||
args['partition'] = index
|
||||
_df = pd.DataFrame(DATA[index],columns=args['columns'])
|
||||
args['reader'] = lambda: _df
|
||||
make = lambda _args: (Components()).train(**_args)
|
||||
job = Process(target=make,args=(args,))
|
||||
job.name = 'Trainer # ' + str(index)
|
||||
job.start()
|
||||
jobs.append(job)
|
||||
# args['gpu']
|
||||
print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
|
||||
while len(jobs)> 0 :
|
||||
jobs = [job for job in jobs if job.is_alive()]
|
||||
time.sleep(2)
|
||||
|
||||
# trainer = Components()
|
||||
# trainer.train(**args)
|
||||
|
||||
|
||||
trainer = Components()
|
||||
trainer.train(**args)
|
||||
# Components.train(**args)
|
||||
#for args in PIPELINE :
|
||||
#args['dataset'] = 'combined20190510'
|
||||
|
|
|
|||
Loading…
Reference in New Issue