data-maker/pipeline.py

#!/usr/bin/env python3
import json
from transport import factory
import numpy as np
import os
from multiprocessing import Process
import pandas as pd
from google.oauth2 import service_account
import data.maker

from data.params import SYS_ARGS 

#
# The configuration array is now loaded and we will execute the pipe line as follows
DATASET='combined20191004v2_deid'

class Components :

	@staticmethod
	def get(args):
		"""
		This function returns a data-frame provided a bigquery sql statement with conditions (and limits for testing purposes)
		The function must be wrapped around a lambda this makes testing easier and changing data stores transparent to the rest of the code. (Vital when testing)
		:sql	basic sql statement
		:condition	optional condition and filters
		"""
		SQL = args['sql']
		if 'condition' in args :
			condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
			SQL = " ".join([SQL,'WHERE',condition])

		SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
		if 'limit' in args :
			SQL = SQL + 'LIMIT ' + args['limit']
		credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
		df = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
		return df
		
		# return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna()
	@staticmethod
	def split(X,MAX_ROWS=3,PART_SIZE=3):
		
		return list(pd.cut( np.arange(X.shape[0]+1),PART_SIZE).categories)

	def train(self,**args):
		"""
		This function will perform training on the basis of a given pointer that reads data

		"""
		#
		# @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 
		#
		# Now we can parse the arguments and submit the entire thing to training
		#
		
		logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
		log_folder = args['logs'] if 'logs' in args else 'logs'
		_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 
		_args['gpu'] = args['gpu'] if 'gpu' in args else 0

		MAX_ROWS = args['max_rows'] 	if 'max_rows' in args else 0
		PART_SIZE = args['part_size'] 	if 'part_size' in args else 0
		
		if df.shape[0] > MAX_ROWS and 'partition' not in args:
			lbound = 0
			bounds = list(pd.cut( np.arange(df.shape[0]+1),PART_SIZE).categories)
			# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
			
			qwriter = factory.instance(type='queue.QueueWriter',args={'queue':'aou.io'})
			
			for b in bounds :
				part_index = bounds.index(b)
				ubound = int(b.right)
				
					
				_data =  df.iloc[lbound:ubound][args['columns']]
				lbound = ubound
				
				# _args['logs'] = os.sep.join([log_folder,str(part_index)])
				_args['partition'] = str(part_index)
				_args['logger'] = {'args':{'dbname':'aou','doc':args['context']},'type':'mongo.MongoWriter'}
				
				#
				# We should post the the partitions to a queue server (at least the instructions on ):
				#	- where to get the data
				#	- and athe arguments to use (partition #,columns,gpu,epochs)
				#
				info = {"rows":_data.shape[0],"cols":_data.shape[1], "paritition":part_index,"logs":_args['logs']}
				p = {"args":_args,"data":_data.to_dict(orient="records"),"info":info}
				qwriter.write(p)
				#
				# @TODO:
				#	- Notify that information was just posted to the queue
				info['max_rows'] 	= MAX_ROWS
				info['part_size'] 	= PART_SIZE
				logger.write({"module":"train","action":"setup-partition","input":info})
			
			pass
		else:
			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['data'] = df
			#
			# @log :
			#	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']
		MAX_GPUS = 8
		args['gpu'] = int(info['info']['partition']) if info['info']['partition'] < MAX_GPUS else np.random.choice(np.arange(MAX_GPUS),1).astype(int).tolist()[0]
		# if int(args['num_gpu']) > 1 and args['gpu'] > 0:
		# 	args['gpu'] = args['gpu'] + args['num_gpu'] if args['gpu'] + args['num_gpu'] < 8 else args['gpu'] #-- 8 max gpus
		args['reader'] = lambda: df
		#
		# @TODO: Fix
		# 	There is an inconsistency in column/columns ... fix this shit!
		#
		args['columns'] = args['column']		
		(Components()).train(**args)		
		logger.write({"module":"process","action":"exit","info":info["info"]})
		channel.close()
		channel.connection.close()
		pass
	
if __name__ == '__main__' :
	filename = SYS_ARGS['config'] if 'config' in SYS_ARGS else 'config.json'
	f = open (filename)
	PIPELINE = json.loads(f.read())
	f.close()	
	index = int(SYS_ARGS['index']) if 'index' in SYS_ARGS else 0
	
	args =  (PIPELINE[index])
	
	args = dict(args,**SYS_ARGS)
	args['max_rows'] = int(args['max_rows']) 	if 'max_rows' in args else 3
	args['part_size']= int(args['part_size']) 	if 'part_size' in args else 3
	if 'dataset' not in args :
		args['dataset'] = 'combined20191004v2_deid'

	#
	# @TODO:
	#	Log what was initiated so we have context of this processing ...
	#
	if 'listen' not in SYS_ARGS :
		if 'file' in args :
			reader = lambda: pd.read_csv(args['file']) ;
		else:
			reader = lambda: Components().get(args)
		args['reader'] = reader
	
	if 'generate' in SYS_ARGS :
		#
		# Let us see if we have partitions given the log folder
		
		content = os.listdir( os.sep.join([args['logs'],args['context']]))
		generator = Components()
		if ''.join(content).isnumeric() :
			#
			# we have partitions we are working with
			
			for id in ''.join(content) :
				args['partition'] = id
				
				generator.generate(args)
		else:
			generator.generate(args)
		# Components.generate(args)
	elif 'listen' in args :
		#
		# This will start a worker just in case to listen to a queue
		SYS_ARGS = dict(args)	#-- things get lost in context
		if 'read' in SYS_ARGS :
			QUEUE_TYPE = 'queue.QueueReader'
			pointer = lambda qreader: qreader.read(1)
		else:
			QUEUE_TYPE = 'queue.QueueListener'
			pointer = lambda qlistener: qlistener.listen()
		N = int(SYS_ARGS['jobs']) if 'jobs' in SYS_ARGS else 1 
		
		qhandlers = [factory.instance(type=QUEUE_TYPE,args={'queue':'aou.io'}) for i in np.arange(N)]
		jobs = []
		for qhandler in qhandlers :
			qhandler.callback = Components.callback
			job = Process(target=pointer,args=(qhandler,))
			job.start()
			jobs.append(job)
		#
		# let us wait for the jobs
		print (["Started ",len(jobs)," trainers"])
		while len(jobs) > 0 :
			
			jobs = [job for job in jobs if job.is_alive()]
		
		# pointer(qhandler)


		# qreader.read(1)
		pass
	else:
		
		trainer = Components()
		trainer.train(**args)
		# Components.train(**args)
#for args in PIPELINE :
	#args['dataset'] = 'combined20190510'
	#process = Process(target=Components.train,args=(args,))
	#process.name = args['context']
	#process.start()
#	Components.train(args)