bug fix: finalize to remove duplicate keys
This commit is contained in:
Steve Nyemba
parent
3eb28dd798
commit
94798fd9a2
123
pipeline.py
123
pipeline.py
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@ -268,7 +268,48 @@ class Components :
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else:
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writer.write(_df[columns],table=args['from'])
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# @staticmethod
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def finalize(self,args):
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"""
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This function performs post-processing opertions on a synthetic table i.e :
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- remove duplicate keys
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- remove orphaned keys i.e
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"""
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reader = factory.instance(**args['store']['source'])
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logger = factory.instance(**args['store']['logs'])
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target = args['store']['target']['args']['dataset']
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source = args['store']['source']['args']['dataset']
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table = args['from']
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schema = reader.meta(table=args['from'])
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#
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# keys :
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unique_field = "_".join([args['from'],'id']) if 'unique_fields' not in args else args['unique_fields']
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fields = [ item.name if item.name != unique_field else "y."+item.name for item in schema]
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SQL = [
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"SELECT :fields FROM ",
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"(SELECT ROW_NUMBER() OVER() AS row_number,* FROM :target.:table) x","INNER JOIN",
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"(SELECT ROW_NUMBER() OVER() AS row_number, :unique_field FROM :source.:table) y",
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"ON y.row_number = x.row_number"
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]
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SQL = " ".join(SQL).replace(":fields",",".join(fields)).replace(":table",table).replace(":source",source).replace(":target",target)
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SQL = SQL.replace(":unique_field",unique_field)
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#
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# Use a native job to get this done ...
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#
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client = bq.Client.from_service_account_json(args['store']['source']['args']["private_key"])
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job = bq.QueryJobConfig()
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job.destination = client.dataset(target).table(table)
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job.use_query_cache = True
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job.allow_large_results = True
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# job.time_partitioning = bq.table.TimePartitioning(type_=bq.table.TimePartitioningType.DAY)
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job.write_disposition = "WRITE_TRUNCATE"
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job.priority = 'BATCH'
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r = client.query(SQL,location='US',job_config=job)
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logger.write({"job":r.job_id,"action":"finalize", "args":{"sql":SQL,"source":"".join([source,table]),"destimation":".".join([target,table])}})
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#
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# Keep a log of what just happened...
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#
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otable = ".".join([args['store']['source']['args']['dataset'],args['from']])
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dtable = ".".join([args['store']['target']['args']['dataset'],args['from']])
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def generate(self,args):
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"""
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This function will generate data and store it to a given,
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@ -527,18 +568,7 @@ if __name__ == '__main__' :
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# @TODO:
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# Log what was initiated so we have context of this processing ...
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#
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# if 'listen' not in SYS_ARGS :
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# if 'file' in args :
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# DATA = pd.read_csv(args['file']) ;
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# schema = []
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# else:
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# DATA = Components().get(args)
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# client = bq.Client.from_service_account_json(args["private_key"])
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# schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
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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 = args['gpu'] if 'gpu' in 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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@ -550,50 +580,6 @@ if __name__ == '__main__' :
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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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# if ''.join(content).isnumeric() :
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# #
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# # we have partitions we are working with
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# jobs = []
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# # columns = DATA.columns.tolist()
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# # DATA = np.array_split(DATA,PART_SIZE)
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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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# #
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# # This handles failures/recoveries for whatever reason
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# # If we are only interested in generating data for a given partition
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# continue
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# # index = id.index(id)
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# args['partition'] = index
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# args['data'] = DATA[index]
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# if int(args['num_gpu']) > 1 :
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# args['gpu'] = index
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# else:
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# args['gpu']=0
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# make = lambda _args: (Components()).generate(_args)
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# job = Process(target=make,args=(args,))
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# job.name = 'generator # '+str(index)
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# job.start()
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# jobs.append(job)
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# # if len(jobs) == 1 :
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# # job.join()
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# print (["Started ",len(jobs),"generators" if len(jobs)>1 else "generator" ])
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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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# # generator.generate(args)
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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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@ -625,7 +611,7 @@ if __name__ == '__main__' :
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shuffler = Components()
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shuffler.shuffle(args)
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pass
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else:
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elif 'train' in SYS_ARGS:
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# DATA = np.array_split(DATA,PART_SIZE)
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#
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@ -657,10 +643,25 @@ if __name__ == '__main__' :
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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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DIRTY = 0
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while len(jobs)> 0 :
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DIRTY =1
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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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if DIRTY:
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print (["..:: jobs finished "])
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#
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# We need to harmonize the keys if any at all in this case we do this for shuffle or generate operations
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#
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print (['finalize' in SYS_ARGS, ('generate' in SYS_ARGS or 'shuffle' in SYS_ARGS) ])
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if 'finalize' in SYS_ARGS or ('generate' in SYS_ARGS or 'shuffle' in SYS_ARGS) :
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#
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# We should pull all the primary keys and regenerate them in order to insure some form of consistency
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#
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(Components()).finalize(args)
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# finalize(args)
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pass
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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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2
setup.py
2
setup.py
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@ -5,7 +5,7 @@ import sys
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def read(fname):
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return open(os.path.join(os.path.dirname(__file__), fname)).read()
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args = {"name":"data-maker",
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"version":"1.4.5",
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"version":"1.4.6",
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"author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
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"packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
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args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo']
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