data-maker/pipeline.py

#!/usr/bin/env python3
			# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
			#	Logging information about the training process for this partition (or not)
			#
			info = {"rows":df.shape[0],"cols":df.shape[1], "partition":partition,"logs":_args['logs']}
			logger.write({"module":"train","action":"train","input":info})
			data.maker.train(**_args)

		pass
		
	# @staticmethod
	def generate(self,args):
		"""
		This function will generate data and store it to a given,
		"""
		logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
		log_folder = args['logs'] if 'logs' in args else 'logs'
		partition = args['partition'] if 'partition' in args else ''
		log_folder = os.sep.join([log_folder,args['context'],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'
		_args['no_value']= args['no_value']
		MAX_ROWS = args['max_rows']  	if 'max_rows' in args else 0
		PART_SIZE = args['part_size'] 	if 'part_size' in args else 0
		
		# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
		# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
		reader = args['reader']
		df = reader()
		if 'partition' in args :
			bounds = Components.split(df,MAX_ROWS,PART_SIZE)
			# bounds = list(pd.cut( np.arange(df.shape[0]+1),PART_SIZE).categories)
			lbound = int(bounds[int(partition)].left)
			ubound = int(bounds[int(partition)].right)
			df = df.iloc[lbound:ubound]
		_args['data'] = df
			# _args['data'] = reader()
		#_args['data'] = _args['data'].astype(object)
		_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1 
		_dc = data.maker.generate(**_args) 
		#
		# We need to post the generate the data in order to :
		#	1. compare immediately
		#	2. synthetic copy
		#
		
		cols = _dc.columns.tolist()
		
		data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io')				#-- will be used for comparison (store this in big query)
		base_cols = list(set(_args['data'].columns) - set(args['columns']))	#-- rebuilt the dataset (and store it)
		
		for name in cols :
			_args['data'][name] = _dc[name]
			info = {"module":"generate","action":"io","input":{"rows":_dc[name].shape[0],"name":name}}
			if partition != '' :
				info['partition'] = partition
			logger.write(info)
			# filename = os.sep.join([log_folder,'output',name+'.csv'])
			# data_comp[[name]].to_csv(filename,index=False)

		#
		#-- Let us store all of this into bigquery
		prefix = args['notify']+'.'+_args['context']
		table = '_'.join([prefix,partition,'io']).replace('__','_')
		folder = os.sep.join([args['logs'],args['context'],partition,'output']) 
		if 'file' in args :
			
			_fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
			_pname = os.sep.join([folder,table])+'.csv'
			data_comp.to_csv( _pname,index=False)
			_args['data'].to_csv(_fname,index=False)
			
			
		else:
			credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
			_pname = os.sep.join([folder,table+'.csv'])
			_fname = table.replace('_io','_full_io')
			data_comp.to_gbq(if_exists='replace',destination_table=_pname,credentials='credentials',chunk_size=50000)	
			data_comp.to_csv(_pname,index=False)
			INSERT_FLAG = 'replace' if 'partition' not in args else 'append'	
			_args['data'].to_gbq(if_exists=INSERT_FLAG,destination_table=_fname,credentials='credentials',chunk_size=50000)
		
		info = {"full":{"path":_fname,"rows":_args['data'].shape[0]},"compare":{"name":_pname,"rows":data_comp.shape[0]} }
		if partition :
			info ['partition'] = partition
		logger.write({"module":"generate","action":"write","info":info} )
	@staticmethod
	def callback(channel,method,header,stream):
		
		info = json.loads(stream)
		logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':SYS_ARGS['context']})
		
		logger.write({'module':'process','action':'read-partition','input':info['info']})
		df = pd.DataFrame(info['data'])
		args = info['args']
	#	Log what was initiated so we have context of this processing ...
		while len(jobs) > 0 :
			
			jobs = [job for job in jobs if job.is_alive()]
		
		# pointer(qhandler)