healthcareio
/
data-maker
mirror of https://hiplab.mc.vanderbilt.edu/git/aou/data-maker.git
1
1
Fork 0
Code Issues Packages Projects Releases Wiki Activity

bug fix

Browse Source
dev
Steve Nyemba 3 years ago
parent becc30ff42
commit 2fdc7c8f5c
1 changed files with 26 additions and 195 deletions
Show Stats Download Patch File Download Diff File

221
data/maker/__init__.py
Unescape View File

@ -21,181 +21,6 @@ import json
from multiprocessing import Process, RLock
from datetime import datetime, timedelta
class ContinuousToDiscrete :
ROUND_UP = 2
@staticmethod
def binary(X,n=4) :
"""
This function will convert a continous stream of information into a variety a bit stream of bins
"""
values = np.array(X).astype(np.float32)
BOUNDS = ContinuousToDiscrete.bounds(values,n)
matrix = np.repeat(np.zeros(n),len(X)).reshape(len(X),n)
@staticmethod
def bounds(x,n):
# return np.array_split(x,n)
values = np.round(x,ContinuousToDiscrete.ROUND_UP)
return list(pd.cut(values,n).categories)
@staticmethod
def continuous(X,BIN_SIZE=4) :
"""
This function will approximate a binary vector given boundary information
:X binary matrix
:BIN_SIZE
"""
BOUNDS = ContinuousToDiscrete.bounds(X,BIN_SIZE)
values = []
# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
# # # print (BOUNDS)
l = {}
for i in np.arange(len(X)): #value in X :
value = X[i]
for item in BOUNDS :
if value >= item.left and value <= item.right :
values += [np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP)]
break
# values += [ np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP) for item in BOUNDS if value >= item.left and value <= item.right ]
# # values = []
# for row in _BINARY :
# # ubound = BOUNDS[row.index(1)]
# index = np.where(row == 1)[0][0]
# ubound = BOUNDS[ index ].right
# lbound = BOUNDS[ index ].left
# x_ = np.round(np.random.uniform(lbound,ubound),ContinuousToDiscrete.ROUND_UP).astype(float)
# values.append(x_)
# lbound = ubound
# values = [np.random.uniform() for item in BOUNDS]
return values
def train (**_args):
"""
:params sql
:params store
"""
_inputhandler = prepare.Input(**_args)
values,_matrix = _inputhandler.convert()
args = {"real":_matrix,"context":_args['context']}
_map = {}
if 'store' in _args :
#
# This
args['store'] = copy.deepcopy(_args['store']['logs'])
if 'args' in _args['store']:
args['store']['args']['doc'] = _args['context']
else:
args['store']['doc'] = _args['context']
logger = transport.factory.instance(**args['store'])
args['logger'] = logger
for key in _inputhandler._map :
beg = _inputhandler._map[key]['beg']
end = _inputhandler._map[key]['end']
values = _inputhandler._map[key]['values'].tolist()
_map[key] = {"beg":beg,"end":end,"values":np.array(values).astype(str).tolist()}
info = {"rows":_matrix.shape[0],"cols":_matrix.shape[1],"map":_map}
print()
# print ([_args['context'],_inputhandler._io])
logger.write({"module":"gan-train","action":"data-prep","context":_args['context'],"input":_inputhandler._io})
args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
args ['max_epochs'] = _args['max_epochs']
args['matrix_size'] = _matrix.shape[0]
args['batch_size'] = 2000
if 'partition' in _args :
args['partition'] = _args['partition']
if 'gpu' in _args :
args['gpu'] = _args['gpu']
# os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
trainer = gan.Train(**args)
#
# @TODO: Write the map.json in the output directory for the logs
#
# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']),'w')
f = open(os.sep.join([trainer.out_dir,'map.json']),'w')
f.write(json.dumps(_map))
f.close()
trainer.apply()
pass
def get(**args):
"""
This function will restore a checkpoint from a persistant storage on to disk
"""
pass
def generate(**_args):
"""
This function will generate a set of records, before we must load the parameters needed
:param data
:param context
:param logs
"""
_args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
partition = _args['partition'] if 'partition' in _args else None
if not partition :
MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context']])
# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
else:
MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context'],str(partition)])
# f = open(os.sep.join([_args['logs'],'output',_args['context'],str(partition),'map.json']))
f = open(os.sep.join([MAP_FOLDER,'map.json']))
_map = json.loads(f.read())
f.close()
#
#
# if 'file' in _args :
# df = pd.read_csv(_args['file'])
# else:
# df = _args['data'] if not isinstance(_args['data'],str) else pd.read_csv(_args['data'])
args = {"context":_args['context'],"max_epochs":_args['max_epochs'],"candidates":_args['candidates']}
args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
args ['max_epochs'] = _args['max_epochs']
# args['matrix_size'] = _matrix.shape[0]
args['batch_size'] = 2000
args['partition'] = 0 if 'partition' not in _args else _args['partition']
args['row_count'] = _args['data'].shape[0]
#
# @TODO: perhaps get the space of values here ... (not sure it's a good idea)
#
_args['map'] = _map
_inputhandler = prepare.Input(**_args)
values,_matrix = _inputhandler.convert()
args['values'] = np.array(values)
if 'gpu' in _args :
args['gpu'] = _args['gpu']
handler = gan.Predict (**args)
lparams = {'columns':None}
if partition :
lparams['partition'] = partition
handler.load_meta(**lparams)
#
# Let us now format the matrices by reverting them to a data-frame with values
#
candidates = handler.apply(candidates=args['candidates'])
return [_inputhandler.revert(matrix=_matrix) for _matrix in candidates]
class Learner(Process):
def __init__(self,**_args):
@ -211,7 +36,7 @@ class Learner(Process):
self.info = _args['info']
self.columns = self.info['columns'] if 'columns' in self.info else None
self.store = _args['store']
self.logger = transport.factory.instance(_args['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
if 'network_args' not in _args :
self.network_args ={
'context':self.info['context'] ,
@ -228,12 +53,18 @@ class Learner(Process):
#
# @TODO: allow for verbose mode so we have a sens of what is going on within the newtork
#
if self.logger :
_args = {'module':self.name,'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
self.logger.write(_args)
_log = {'module':self.name,'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
self.log(**_log)
# self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
# sel.max_epoc
def log(self,**_args):
logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
logger.write(_args)
if hasattr(logger,'close') :
logger.close()
def get_schema(self):
if self.store['source']['provider'] != 'bigquery' :
return [{'name':self._df.dtypes.index.tolist()[i],'type':self._df.dtypes.astype(str).tolist()[i]}for i in range(self._df.dtypes.shape[0])]
@ -253,9 +84,9 @@ class Learner(Process):
if self._map :
_args['map'] = self._map
self._encoder = prepare.Input(**_args) if self._df.shape[0] > 0 else None
if self.logger :
_args = {'module':self.name,'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
self.logger.write(_args)
_log = {'module':self.name,'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
self.log(**_log)
class Trainer(Learner):
"""
This will perform training using a GAN
@ -301,10 +132,10 @@ class Trainer(Learner):
_args['gpu'] = self.gpu
g = Generator(**_args)
# g.run()
if self.logger :
end = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
logs = {'module':self.name,'action':'train','input':{'start':beg,'end':end}}
self.logger.write(logs)
end = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
_logs = {'module':self.name,'action':'train','input':{'start':beg,'end':end}}
self.log(**_logs)
self.generate = g
if self.autopilot :
self.generate.run()
@ -347,10 +178,10 @@ class Generator (Learner):
gHandler.load_meta(columns=None)
_iomatrix = gHandler.apply()
_candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix]
if self.logger :
_size = np.sum([len(_item) for _item in _iomatrix])
_log = {'module':self.name,'action':'io-data','input':{'candidates':len(_candidates),'rows':_size}}
self.logger.write(_log)
_size = np.sum([len(_item) for _item in _iomatrix])
_log = {'module':self.name,'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
self.log(**_log)
self.post(_candidates)
def approximate(self,_df):
_columns = self.info['approximate']
@ -373,10 +204,10 @@ class Generator (Learner):
values[index] = values[index].astype(_type)
x += values.tolist()
if x :
_log['input']['diff'] = 1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size)
_log['input']['diff_pct'] = 100 * (1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size))
_df[name] = x #np.array(x,dtype=np.int64) if 'int' in _type else np.arry(x,dtype=np.float64)
if self.logger :
self.logger.write(_log)
self.log(**_log)
return _df
def make_date(self,**_args) :
"""
@ -446,8 +277,8 @@ class Generator (Learner):
_schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
writer.write(_df,schema=_schema)
if self.logger :
self.logger.write({'module':self.name,'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
self.log(**{'module':self.name,'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
class factory :
_infocache = {}
@staticmethod

Write Preview
Loading…
Cancel
Save