data-maker/pipeline.py

#!/usr/bin/env python3
import json
from transport import factory
import numpy as np
import time
import os
from multiprocessing import Process, Lock
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 :
	lock = Lock()
	class KEYS :
		PIPELINE_KEY = 'pipeline'
		SQL_FILTER = 'filter'
	@staticmethod
	def get_filter (**args):
		if args['qualifier'] == 'IN' :
			return ' '.join([args['field'],args['qualifier'],'(',args['value'],')'])
		else:
			return ' '.join([args['field'],args['qualifier'],args['value']])
	@staticmethod
	def get_logger(**args) :
		return factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
	@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 Components.KEYS.SQL_FILTER in args :
			FILTER_KEY = Components.KEYS.SQL_FILTER
			SQL_FILTER = args[FILTER_KEY] if type(args[FILTER_KEY]) == list else [args[FILTER_KEY]]
			# condition = ' '.join([args[FILTER_KEY]['field'],args[FILTER_KEY]['qualifier'],'(',args[FILTER_KEY]['value'],')'])
			
			condition = ' AND '.join([Components.get_filter(**item) for item in SQL_FILTER])
			SQL = " ".join([SQL,'WHERE',condition])

		SQL = SQL.replace(':dataset',args['dataset']) #+ " LI "
		
		if 'limit' in args :
			SQL = SQL + ' LIMIT ' + args['limit']
		#
		# let's log the sql query that has been performed here
		logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
		logger.write({"module":"bigquery","action":"read","input":{"sql":SQL}})
		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 = args['data']
		
		
		# 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'
		PART_SIZE = int(args['part_size']) 

		partition = args['partition'] 
		log_folder = os.sep.join([log_folder,args['context'],str(partition)])
		_args = {"batch_size":2000,"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'])
		if 'batch_size' in args :
			_args['batch_size'] = int(args['batch_size'])
			
		#
		# We ask the process to assume 1 gpu given the system number of GPU and that these tasks can run in parallel
		#
		if int(args['num_gpu']) > 1 :
			_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
		else:
			_args['gpu']  = 0
		_args['num_gpu'] 	= 1
		os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) 
		_args['partition'] = int(partition)
		_args['continuous']= args['continuous'] if 'continuous' in args else []
		_args['store'] = {'type':'mongo.MongoWriter','args':{'dbname':'aou','doc':args['context']}}
		_args['data'] = args['data']
		
		# print (['partition ',partition,df.value_source_concept_id.unique()])
		#
		# @log :
		#	Logging information about the training process for this partition (or not)
		#
		
		info = {"rows":df.shape[0],"cols":df.shape[1], "partition":int(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'],str(partition)])
		
		_args = {"batch_size":2000,"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 
		if 'batch_size' in args :
			_args['batch_size'] = int(args['batch_size'])
		
		if int(args['num_gpu']) > 1 :
			_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
		else:
			_args['gpu']  = 0
		_args['num_gpu'] 	= 1
		os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) 
		_args['no_value']= args['no_value']
		
		# MAX_ROWS = args['max_rows']  	if 'max_rows' in args else 0
		PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
		
		# 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()
		df = args['reader']() if 'reader' in args else args['data']
		# bounds = Components.split(df,MAX_ROWS,PART_SIZE)
		# if partition != '' :
		# 	columns = args['columns']
		# 	df = np.array_split(df[columns].values,PART_SIZE)
		# 	df = pd.DataFrame(df[ int (partition) ],columns = columns)
		# max_rows = int(args['partition_max_rows']) if 'partition_max_rows' in args else 1000000
		# N = np.divide(df.shape[0],max_rows).astype(int) + 1
		info = {"parition":int(partition),"gpu":_args["gpu"],"rows":int(df.shape[0]),"cols":int(df.shape[1]),"part_size":int(PART_SIZE),"partition-info":{"count":int(N),"max_rows":max_rows}}
		logger.write({"module":"generate","action":"partition","input":info})
		_args['partition'] = int(partition)
		_args['continuous']= args['continuous'] if 'continuous' in args else []
		#
		# How many rows sub-partition must we divide this into ?
		# -- Let us tray assessing
		
		
		df = np.array_split(df,N)
		_dc = pd.DataFrame()
		# for mdf in df :
		_args['data'] = df		
		_dc = _dc.append(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)
		#
		# performing basic analytics on the synthetic data generated (easy to quickly asses)
		#
		info = {"module":"generate","action":"io.metrics","input":{"rows":data_comp.shape[0],"partition":partition,"logs":[]}}
		x = {}
		#
		# @TODO: Send data over to a process for analytics
		
		base_cols = list(set(_args['data'].columns) - set(args['columns']))	#-- rebuilt the dataset (and store it)
		cols = _dc.columns.tolist()
		for name in cols :
			_args['data'][name] = _dc[name]

		#
		#-- Let us store all of this into bigquery
		prefix = args['notify']+'.'+_args['context']
		partition = str(partition)
		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)
			
			_id = 'path'
		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')
			partial = '.'.join(['io',args['context']+'_partial_io'])
			complete= '.'.join(['io',args['context']+'_full_io'])
			data_comp.to_csv(_pname,index=False)
			if 'dump' in args :
				print (_args['data'].head())
			else:
				Components.lock.acquire()
				data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)	
				
				INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append'	
				_args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000)
				Components.lock.release()
			_id = 'dataset'
		info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
		if partition :
			info ['partition'] = int(partition)
		logger.write({"module":"generate","action":"write","input":info} )
		
	
	
if __name__ == '__main__' :
	filename = SYS_ARGS['config'] if 'config' in SYS_ARGS else 'config.json'
	f = open (filename)
	_config = json.loads(f.read())
	f.close()	
	PIPELINE = _config['pipeline']
	index = SYS_ARGS['index']
	if index.isnumeric() :
		index = int(SYS_ARGS['index']) 
	else:
		#
		# The index provided is a key to a pipeline entry mainly the context
		#
		N = len(PIPELINE)
		f = [i for i in range(0,N) if PIPELINE[i]['context'] == index]
		index = f[0] if f else 0
	#
	
	print ("..::: ",PIPELINE[index]['context'])
	args =  (PIPELINE[index])
	for key in _config :
		if key == 'pipeline' or key in args:
			#
			# skip in case of pipeline or if key exists in the selected pipeline (provided by index)
			# 
			continue
		args[key] = _config[key]
	
	args = dict(args,**SYS_ARGS)
	if 'batch_size' not in args :
		args['batch_size']	= 2000 #if 'batch_size' not in args else int(args['batch_size'])
	if 'dataset' not in args :
		args['dataset'] = 'combined20191004v2_deid'
	PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
	#
	# @TODO:
	#	Log what was initiated so we have context of this processing ...
	#
	# if 'listen' not in SYS_ARGS :
	if 'file' in args :
		DATA = pd.read_csv(args['file']) ;
	else:
		DATA = Components().get(args)
	COLUMNS = DATA.columns
	DATA = np.array_split(DATA,PART_SIZE)
	
	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
			
			jobs = []
			
			# columns = DATA.columns.tolist()
			
			# DATA  = np.array_split(DATA,PART_SIZE)

			for index in range(0,PART_SIZE) :
				if 'focus' in args and int(args['focus']) != index :
					#
					# This handles failures/recoveries for whatever reason
					# If we are only interested in generating data for a given partition 
					continue
				# index = id.index(id)
				
				args['partition'] = index
				args['data'] = DATA[index]
				if int(args['num_gpu']) > 1 :
					args['gpu'] = index
				else:
					args['gpu']=0

				make = lambda _args: (Components()).generate(_args)
				job = Process(target=make,args=(args,))
				job.name = 'generator # '+str(index)
				job.start()
				jobs.append(job)
				# if len(jobs) == 1 :
				# 	job.join()

			print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
			while len(jobs)> 0 :
				jobs = [job for job in jobs if job.is_alive()]
				time.sleep(2)
				
				# generator.generate(args)
		else:
			generator.generate(args)
		# Components.generate(args)
	elif 'finalize' in args :
		#
		# This will finalize a given set of synthetic operations into a table
		#
		idataset = args['input'] if 'input' in args else 'io'	#-- input dataset
		odataset = args['output']	#-- output dataset
		labels = [name.strip() for name in args['labels'].split(',') ]
		
	else:
		
		# DATA  = np.array_split(DATA,PART_SIZE)
		
		jobs = []
		for index in range(0,PART_SIZE) :
			if 'focus' in args and int(args['focus']) != index :
				continue
			args['part_size'] = PART_SIZE
			args['partition'] = index
			args['data'] = DATA[index]
			if int(args['num_gpu']) > 1 :
				args['gpu'] = index
			else:
				args['gpu']=0

			make = lambda _args: (Components()).train(**_args)
			job = Process(target=make,args=( dict(args),))
			job.name = 'Trainer # ' + str(index)
			job.start()
			jobs.append(job)
			# args['gpu']
		print (["Started ",len(jobs),"trainers" if len(jobs)>1 else "trainer" ])
		while len(jobs)> 0 :
			jobs = [job for job in jobs if job.is_alive()]
			time.sleep(2)

		# 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)