Steve L. Nyemba
6 years ago
parent
3fbd68309f
commit
a2988a5972
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import json
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from transport import factory
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import os
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from multiprocessing import Process
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import pandas as pd
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from google.oauth2 import service_account
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import data.maker
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from data.params import SYS_ARGS
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f = open ('config.json')
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PIPELINE = json.loads(f.read())
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f.close()
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#
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# The configuration array is now loaded and we will execute the pipe line as follows
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DATASET='combined20190510_deid'
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class Components :
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@staticmethod
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def get(args):
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SQL = args['sql']
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if 'condition' in args :
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condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
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SQL = " ".join([SQL,'WHERE',condition])
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SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
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return SQL #+ " LIMIT 10000 "
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@staticmethod
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def train(args):
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"""
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This function will instanciate a worker that will train given a message that is provided to it
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This is/will be a separate process that will
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"""
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print (['starting .... ',args['notify'],args['context']] )
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#SQL = args['sql']
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#if 'condition' in args :
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# condition = ' '.join([args['condition']['field'],args['condition']['qualifier'],'(',args['condition']['value'],')'])
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# SQL = " ".join([SQL,'WHERE',condition])
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print ( args['context'])
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = os.sep.join(["logs",args['context']])
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_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":250,"num_gpus":2,"column":args['columns'],"id":"person_id","logger":logger}
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os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu']
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#SQL = SQL.replace(':dataset',args['dataset']) #+ " LIMIT 1000 "
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SQL = Components.get(args)
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if 'limit' in args :
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SQL = ' '.join([SQL,'limit',args['limit'] ])
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_args['max_epochs'] = 250 if 'max_epochs' not in args else args['max_epochs']
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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#_args['data'] = _args['data'].astype(object)
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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data.maker.train(**_args)
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@staticmethod
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def generate(args):
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"""
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This function will generate data and store it to a given,
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"""
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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log_folder = os.sep.join(["logs",args['context']])
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_args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":250,"num_gpus":2,"column":args['columns'],"id":"person_id","logger":logger}
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os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu']
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SQL = Components.get(args)
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if 'limit' in args :
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SQL = " ".join([SQL ,'limit', args['limit'] ])
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').fillna('')
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#_args['data'] = _args['data'].astype(object)
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_args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1
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_args['max_epochs'] = 250 if 'max_epochs' not in args else args['max_epochs']
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_args['no_value'] = args['no_value'] if 'no_value' in args else ''
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#credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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#_args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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#_args['data'] = _args['data'].astype(object)
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_dc = data.maker.generate(**_args)
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#
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# We need to post the generate the data in order to :
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# 1. compare immediately
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# 2. synthetic copy
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#
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cols = _dc.columns.tolist()
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print (args['columns'])
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data_comp = _args['data'][args['columns']].join(_dc[args['columns']],rsuffix='_io') #-- will be used for comparison (store this in big query)
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base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
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print (_args['data'].shape)
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print (_args['data'].shape)
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for name in cols :
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_args['data'][name] = _dc[name]
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# filename = os.sep.join([log_folder,'output',name+'.csv'])
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# data_comp[[name]].to_csv(filename,index=False)
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#
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#-- Let us store all of this into bigquery
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prefix = args['notify']+'.'+_args['context']
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table = '_'.join([prefix,'compare','io'])
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data_comp.to_gbq(if_exists='replace',destination_table=table,credentials=credentials,chunksize=50000)
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_args['data'].to_gbq(if_exists='replace',destination_table=table.replace('compare','full'),credentials=credentials,chunksize=50000)
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data_comp.to_csv(os.sep.join([log_folder,table+'.csv']),index=False)
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if __name__ == '__main__' :
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index = int(SYS_ARGS['index'])
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args = (PIPELINE[index])
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#if 'limit' in SYS_ARGS :
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# args['limit'] = SYS_ARGS['limit']
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#args['dataset'] = 'combined20190510'
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SYS_ARGS['dataset'] = 'combined20190510_deid' if 'dataset' not in SYS_ARGS else SYS_ARGS['dataset']
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#if 'max_epochs' in SYS_ARGS :
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# args['max_epochs'] = SYS_ARGS['max_epochs']
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args = dict(args,**SYS_ARGS)
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if 'generate' in SYS_ARGS :
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Components.generate(args)
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else:
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Components.train(args)
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#for args in PIPELINE :
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#args['dataset'] = 'combined20190510'
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#process = Process(target=Components.train,args=(args,))
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#process.name = args['context']
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#process.start()
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# Components.train(args)
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