bug fix
This commit is contained in:
Steve Nyemba
parent
becc30ff42
commit
2fdc7c8f5c
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@ -21,181 +21,6 @@ import json
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from multiprocessing import Process, RLock
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from datetime import datetime, timedelta
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class ContinuousToDiscrete :
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ROUND_UP = 2
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@staticmethod
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def binary(X,n=4) :
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"""
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This function will convert a continous stream of information into a variety a bit stream of bins
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"""
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values = np.array(X).astype(np.float32)
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BOUNDS = ContinuousToDiscrete.bounds(values,n)
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matrix = np.repeat(np.zeros(n),len(X)).reshape(len(X),n)
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@staticmethod
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def bounds(x,n):
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# return np.array_split(x,n)
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values = np.round(x,ContinuousToDiscrete.ROUND_UP)
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return list(pd.cut(values,n).categories)
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@staticmethod
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def continuous(X,BIN_SIZE=4) :
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"""
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This function will approximate a binary vector given boundary information
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:X binary matrix
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:BIN_SIZE
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"""
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BOUNDS = ContinuousToDiscrete.bounds(X,BIN_SIZE)
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values = []
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# _BINARY= ContinuousToDiscrete.binary(X,BIN_SIZE)
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# # # print (BOUNDS)
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l = {}
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for i in np.arange(len(X)): #value in X :
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value = X[i]
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for item in BOUNDS :
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if value >= item.left and value <= item.right :
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values += [np.round(np.random.uniform(item.left,item.right),ContinuousToDiscrete.ROUND_UP)]
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break
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# 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 ]
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# # values = []
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# for row in _BINARY :
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# # ubound = BOUNDS[row.index(1)]
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# index = np.where(row == 1)[0][0]
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# ubound = BOUNDS[ index ].right
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# lbound = BOUNDS[ index ].left
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# x_ = np.round(np.random.uniform(lbound,ubound),ContinuousToDiscrete.ROUND_UP).astype(float)
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# values.append(x_)
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# lbound = ubound
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# values = [np.random.uniform() for item in BOUNDS]
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return values
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def train (**_args):
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"""
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:params sql
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:params store
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"""
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_inputhandler = prepare.Input(**_args)
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values,_matrix = _inputhandler.convert()
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args = {"real":_matrix,"context":_args['context']}
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_map = {}
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if 'store' in _args :
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#
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# This
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args['store'] = copy.deepcopy(_args['store']['logs'])
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if 'args' in _args['store']:
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args['store']['args']['doc'] = _args['context']
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else:
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args['store']['doc'] = _args['context']
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logger = transport.factory.instance(**args['store'])
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args['logger'] = logger
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for key in _inputhandler._map :
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beg = _inputhandler._map[key]['beg']
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end = _inputhandler._map[key]['end']
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values = _inputhandler._map[key]['values'].tolist()
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_map[key] = {"beg":beg,"end":end,"values":np.array(values).astype(str).tolist()}
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info = {"rows":_matrix.shape[0],"cols":_matrix.shape[1],"map":_map}
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print()
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# print ([_args['context'],_inputhandler._io])
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logger.write({"module":"gan-train","action":"data-prep","context":_args['context'],"input":_inputhandler._io})
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args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
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args ['max_epochs'] = _args['max_epochs']
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args['matrix_size'] = _matrix.shape[0]
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args['batch_size'] = 2000
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if 'partition' in _args :
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args['partition'] = _args['partition']
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if 'gpu' in _args :
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args['gpu'] = _args['gpu']
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# os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) if 'gpu' in args else '0'
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trainer = gan.Train(**args)
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#
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# @TODO: Write the map.json in the output directory for the logs
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#
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# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']),'w')
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f = open(os.sep.join([trainer.out_dir,'map.json']),'w')
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f.write(json.dumps(_map))
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f.close()
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trainer.apply()
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pass
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def get(**args):
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"""
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This function will restore a checkpoint from a persistant storage on to disk
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"""
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pass
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def generate(**_args):
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"""
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This function will generate a set of records, before we must load the parameters needed
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:param data
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:param context
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:param logs
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"""
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_args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
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partition = _args['partition'] if 'partition' in _args else None
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if not partition :
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MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context']])
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# f = open(os.sep.join([_args['logs'],'output',_args['context'],'map.json']))
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else:
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MAP_FOLDER = os.sep.join([_args['logs'],'output',_args['context'],str(partition)])
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# f = open(os.sep.join([_args['logs'],'output',_args['context'],str(partition),'map.json']))
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f = open(os.sep.join([MAP_FOLDER,'map.json']))
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_map = json.loads(f.read())
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f.close()
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#
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#
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# if 'file' in _args :
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# df = pd.read_csv(_args['file'])
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# else:
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# df = _args['data'] if not isinstance(_args['data'],str) else pd.read_csv(_args['data'])
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args = {"context":_args['context'],"max_epochs":_args['max_epochs'],"candidates":_args['candidates']}
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args['logs'] = _args['logs'] if 'logs' in _args else 'logs'
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args ['max_epochs'] = _args['max_epochs']
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# args['matrix_size'] = _matrix.shape[0]
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args['batch_size'] = 2000
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args['partition'] = 0 if 'partition' not in _args else _args['partition']
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args['row_count'] = _args['data'].shape[0]
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#
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# @TODO: perhaps get the space of values here ... (not sure it's a good idea)
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#
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_args['map'] = _map
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_inputhandler = prepare.Input(**_args)
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values,_matrix = _inputhandler.convert()
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args['values'] = np.array(values)
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if 'gpu' in _args :
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args['gpu'] = _args['gpu']
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handler = gan.Predict (**args)
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lparams = {'columns':None}
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if partition :
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lparams['partition'] = partition
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handler.load_meta(**lparams)
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#
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# Let us now format the matrices by reverting them to a data-frame with values
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#
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candidates = handler.apply(candidates=args['candidates'])
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return [_inputhandler.revert(matrix=_matrix) for _matrix in candidates]
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class Learner(Process):
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def __init__(self,**_args):
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@ -211,7 +36,7 @@ class Learner(Process):
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self.info = _args['info']
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self.columns = self.info['columns'] if 'columns' in self.info else None
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self.store = _args['store']
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self.logger = transport.factory.instance(_args['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
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if 'network_args' not in _args :
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self.network_args ={
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'context':self.info['context'] ,
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@ -228,12 +53,18 @@ class Learner(Process):
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#
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# @TODO: allow for verbose mode so we have a sens of what is going on within the newtork
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#
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if self.logger :
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_args = {'module':self.name,'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
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self.logger.write(_args)
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_log = {'module':self.name,'action':'init','context':self.info['context'],'gpu':(self.gpu if self.gpu is not None else -1)}
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self.log(**_log)
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# self.logpath= _args['logpath'] if 'logpath' in _args else 'logs'
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# sel.max_epoc
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def log(self,**_args):
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logger = transport.factory.instance(**self.store['logger']) if 'logger' in self.store else transport.factory.instance(provider='console',context='write',lock=True)
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logger.write(_args)
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if hasattr(logger,'close') :
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logger.close()
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def get_schema(self):
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if self.store['source']['provider'] != 'bigquery' :
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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])]
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@ -253,9 +84,9 @@ class Learner(Process):
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if self._map :
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_args['map'] = self._map
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self._encoder = prepare.Input(**_args) if self._df.shape[0] > 0 else None
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if self.logger :
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_args = {'module':self.name,'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
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self.logger.write(_args)
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_log = {'module':self.name,'action':'data-prep','input':{'rows':self._df.shape[0],'cols':self._df.shape[1]} }
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self.log(**_log)
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class Trainer(Learner):
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"""
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This will perform training using a GAN
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@ -301,10 +132,10 @@ class Trainer(Learner):
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_args['gpu'] = self.gpu
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g = Generator(**_args)
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# g.run()
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if self.logger :
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end = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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logs = {'module':self.name,'action':'train','input':{'start':beg,'end':end}}
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self.logger.write(logs)
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_logs = {'module':self.name,'action':'train','input':{'start':beg,'end':end}}
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self.log(**_logs)
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self.generate = g
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if self.autopilot :
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self.generate.run()
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@ -347,10 +178,10 @@ class Generator (Learner):
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gHandler.load_meta(columns=None)
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_iomatrix = gHandler.apply()
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_candidates= [ self._encoder.revert(matrix=_item) for _item in _iomatrix]
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if self.logger :
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_size = np.sum([len(_item) for _item in _iomatrix])
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_log = {'module':self.name,'action':'io-data','input':{'candidates':len(_candidates),'rows':_size}}
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self.logger.write(_log)
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_log = {'module':self.name,'action':'io-data','input':{'candidates':len(_candidates),'rows':int(_size)}}
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self.log(**_log)
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self.post(_candidates)
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def approximate(self,_df):
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_columns = self.info['approximate']
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@ -373,10 +204,10 @@ class Generator (Learner):
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values[index] = values[index].astype(_type)
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x += values.tolist()
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if x :
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_log['input']['diff'] = 1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size)
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_log['input']['diff_pct'] = 100 * (1 - np.divide( (_df[name].dropna() == x).sum(),_df[name].dropna().size))
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_df[name] = x #np.array(x,dtype=np.int64) if 'int' in _type else np.arry(x,dtype=np.float64)
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if self.logger :
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self.logger.write(_log)
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self.log(**_log)
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return _df
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def make_date(self,**_args) :
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"""
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@ -446,8 +277,8 @@ class Generator (Learner):
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_schema = [{'name':_item.name,'type':_item.field_type} for _item in _schema]
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writer.write(_df,schema=_schema)
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if self.logger :
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self.logger.write({'module':self.name,'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
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self.log(**{'module':self.name,'action':'write','input':{'rows':N,'candidates':len(_candidates)}})
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class factory :
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_infocache = {}
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@staticmethod
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