feature: bootstrap-like with candidates
This commit is contained in:
Steve Nyemba
parent
12d7573ba8
commit
f26795387e
26
data/gan.py
26
data/gan.py
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@ -67,8 +67,9 @@ class GNet :
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self.NUM_GPUS = 0
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else:
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self.NUM_GPUS = len(self.GPU_CHIPS)
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# os.environ['CUDA_VISIBLE_DEVICES'] = str(self.GPU_CHIPS[0])
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self.PARTITION = args['partition']
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self.PARTITION = args['partition'] if 'partition' in args else None
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# if self.NUM_GPUS > 1 :
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# os.environ['CUDA_VISIBLE_DEVICES'] = "4"
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@ -117,9 +118,14 @@ class GNet :
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for key in ['train','output'] :
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self.mkdir(os.sep.join([self.log_dir,key]))
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
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if 'partition' in args :
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT,str(args['partition'])]))
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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if 'partition' in args :
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self.train_dir = os.sep.join([self.train_dir,str(args['partition'])])
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self.out_dir = os.sep.join([self.out_dir,str(args['partition'])])
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# if self.logger :
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# We will clear the logs from the data-store
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@ -130,7 +136,7 @@ class GNet :
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# db.backup.insert({'name':column,'logs':list(db[column].find()) })
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# db[column].drop()
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def load_meta(self,column):
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def load_meta(self,**args):
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"""
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This function is designed to accomodate the uses of the sub-classes outside of a strict dependency model.
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Because prediction and training can happen independently
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@ -145,6 +151,9 @@ class GNet :
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setattr(self,key,value)
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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if 'partition' in args :
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self.train_dir = os.sep.join([self.train_dir,str(args['partition'])])
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self.out_dir = os.sep.join([self.out_dir,str(args['partition'])])
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def log_meta(self,**args) :
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@ -265,9 +274,9 @@ class Generator (GNet):
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#tf.add_to_collection('glosses', loss)
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tf.compat.v1.add_to_collection('glosses', loss)
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return loss, loss
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def load_meta(self, column):
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super().load_meta(column)
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self.discriminator.load_meta(column)
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def load_meta(self, **args):
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super().load_meta(**args)
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self.discriminator.load_meta(**args)
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def network(self,**args) :
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"""
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This function will build the network that will generate the synthetic candidates
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@ -454,6 +463,7 @@ class Train (GNet):
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# - determine if the GPU/CPU are busy
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#
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for i in self.GPU_CHIPS : #range(self.NUM_GPUS):
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with tf.device('/gpu:%d' % i):
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with tf.name_scope('%s_%d' % ('TOWER', i)) as scope:
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if self._LABEL is not None :
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@ -559,9 +569,9 @@ class Predict(GNet):
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# self.MISSING_VALUES = args['no_value']
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# self.MISSING_VALUES = int(args['no_value']) if args['no_value'].isnumeric() else np.na if args['no_value'] in ['na','NA','N/A'] else args['no_value']
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def load_meta(self, column):
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super().load_meta(column)
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self.generator.load_meta(column)
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def load_meta(self, **args):
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super().load_meta(**args)
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self.generator.load_meta(**args)
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self.ROW_COUNT = self.oROW_COUNT
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def apply(self,**args):
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suffix = self.CONTEXT #self.get.suffix()
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@ -112,7 +112,8 @@ def train (**_args):
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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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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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@ -121,7 +122,8 @@ def 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([_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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@ -140,7 +142,11 @@ def generate(**_args):
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:param context
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:param logs
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"""
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partition = _args['partition'] if 'partition' in _args else None
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if not partition :
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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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f = open(os.sep.join([_args['logs'],'output',_args['context'],str(partition),'map.json']))
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_map = json.loads(f.read())
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f.close()
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# if 'file' in _args :
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@ -165,7 +171,7 @@ def generate(**_args):
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args['gpu'] = _args['gpu']
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handler = gan.Predict (**args)
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handler.load_meta(None)
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handler.load_meta(column=None)
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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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@ -237,7 +237,7 @@ class Input :
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#
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# @NOTE: For some reason, there is an out of memory error created here, this seems to fix it (go figure)
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#
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_matrix = np.array([np.repeat(0,cols.size) for i in range(row_count)])
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_matrix = np.array([np.repeat(0,cols.size) for i in range(0,row_count)])
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[np.put(_matrix[i], np.where(cols == rows[i]) ,1)for i in np.arange(row_count) if np.where(cols == rows[i])[0].size > 0]
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# else:
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# _matrix = cp.zeros([row_count,cols.size])
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63
pipeline.py
63
pipeline.py
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@ -146,6 +146,8 @@ class Components :
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_args['data'] = _args['data'][list(set(_args['data'].columns) - set(x_cols))]
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if 'gpu' in args :
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_args['gpu'] = self.set_gpu(gpu=args['gpu'])
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if 'partition' in args :
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_args['partition'] = args['partition']
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if df.shape[0] and df.shape[0] :
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#
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# We have a full blown matrix to be processed
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@ -171,6 +173,7 @@ class Components :
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r = np.random.dirichlet(values+.001) #-- dirichlet doesn't work on values with zeros
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_sd = values[values > 0].std()
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_me = values[values > 0].mean()
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_mi = values.min()
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x = []
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_type = values.dtype
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for index in np.arange(values.size) :
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@ -182,7 +185,7 @@ class Components :
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value = values[index] - (values[index] * r[index])
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#
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# randomly shifting the measurements
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if np.random.choice([0,1],1)[0] and _me > _sd:
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if np.random.choice([0,1],1)[0] and _me > _sd :
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if np.random.choice([0,1],1)[0] :
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value = value * np.divide(_me,_sd)
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else:
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@ -273,6 +276,9 @@ class Components :
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args['candidates'] = 1 if 'candidates' not in args else int(args['candidates'])
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if 'gpu' in args :
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args['gpu'] = self.set_gpu(gpu=args['gpu'])
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# if 'partition' in args :
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# args['logs'] = os.sep.join([args['logs'],str(args['partition'])])
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_info = {"module":"gan-prep","action":"prune","shape":{"rows":args['data'].shape[0],"columns":args['data'].shape[1]}}
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logger.write(_info)
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if args['data'].shape[0] > 0 and args['data'].shape[1] > 0 :
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@ -459,12 +465,18 @@ if __name__ == '__main__' :
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# COLUMNS = DATA.columns
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# DATA = np.array_split(DATA,PART_SIZE)
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# args['schema'] = schema
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GPU_CHIPS = SYS_ARGS['gpu'] if 'gpu' in SYS_ARGS else None
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if GPU_CHIPS and type(GPU_CHIPS) != list :
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GPU_CHIPS = [int(_id.strip()) for _id in GPU_CHIPS.split(',')] if type(GPU_CHIPS) == str else [GPU_CHIPS]
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if 'gpu' in SYS_ARGS :
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args['gpu'] = GPU_CHIPS
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jobs = []
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if 'generate' in SYS_ARGS :
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#
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# Let us see if we have partitions given the log folder
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content = os.listdir( os.sep.join([args['logs'],'train',args['context']]))
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generator = Components()
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# if ''.join(content).isnumeric() :
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# #
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@ -508,13 +520,60 @@ if __name__ == '__main__' :
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# else:
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# generator.generate(args)
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# Components.generate(args)
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if '--all-chips' in SYS_ARGS and GPU_CHIPS:
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index = 0
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jobs = []
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for _id in GPU_CHIPS :
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_args = copy.deepcopy(args)
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_args['gpu'] = [int(_gpu)]
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_args['partition'] = index
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index += 1
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make = lambda _params: (Components()).generate(_params)
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job = Process(target=make,args=( dict(_args),))
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job.name = 'Trainer # ' + str(index)
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job.start()
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jobs.append(job)
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pass
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else:
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generator = Components()
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generator.generate(args)
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else:
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# DATA = np.array_split(DATA,PART_SIZE)
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#
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# Let us create n-jobs across n-gpus, The assumption here is the data that is produced will be a partition
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# @TODO: Find better name for partition
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#
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if GPU_CHIPS and '--all-chips' in SYS_ARGS:
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index = 0
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for _gpu in GPU_CHIPS :
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_args = copy.deepcopy(args)
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_args['gpu'] = [int(_gpu)]
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_args['partition'] = index
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index += 1
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make = lambda _params: (Components()).train(**_params)
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job = Process(target=make,args=( dict(_args),))
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job.name = 'Trainer # ' + str(index)
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job.start()
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jobs.append(job)
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else:
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#
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# The choice of the chip will be made internally
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agent = Components()
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agent.train(**args)
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#
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# If we have any obs we should wait till they finish
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#
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while len(jobs)> 0 :
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jobs = [job for job in jobs if job.is_alive()]
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time.sleep(2)
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# jobs = []
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# for index in range(0,PART_SIZE) :
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# if 'focus' in args and int(args['focus']) != index :
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