693 lines
23 KiB
Python
693 lines
23 KiB
Python
#!/usr/bin/env python3
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import json
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from transport import factory
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import numpy as np
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import time
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import os
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from multiprocessing import Process, Lock
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import pandas as pd
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from google.oauth2 import service_account
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from google.cloud import bigquery as bq
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import data.maker
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import copy
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from data.params import SYS_ARGS
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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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class Components :
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lock = Lock()
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class KEYS :
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PIPELINE_KEY = 'pipeline'
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SQL_FILTER = 'filter'
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@staticmethod
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def get_filter (**args):
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if args['qualifier'] == 'IN' :
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return ' '.join([args['field'],args['qualifier'],'(',args['value'],')'])
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else:
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return ' '.join([args['field'],args['qualifier'],args['value']])
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@staticmethod
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def get_logger(**args) :
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return factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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@staticmethod
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def get(args):
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"""
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This function returns a data-frame provided a bigquery sql statement with conditions (and limits for testing purposes)
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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)
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:sql basic sql statement
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:condition optional condition and filters
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"""
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SQL = args['sql']
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if Components.KEYS.SQL_FILTER in args :
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FILTER_KEY = Components.KEYS.SQL_FILTER
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SQL_FILTER = args[FILTER_KEY] if type(args[FILTER_KEY]) == list else [args[FILTER_KEY]]
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# condition = ' '.join([args[FILTER_KEY]['field'],args[FILTER_KEY]['qualifier'],'(',args[FILTER_KEY]['value'],')'])
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condition = ' AND '.join([Components.get_filter(**item) for item in SQL_FILTER])
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SQL = " ".join([SQL,'WHERE',condition])
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SQL = SQL.replace(':dataset',args['dataset']) #+ " LI "
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if 'limit' in args :
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SQL = SQL + ' LIMIT ' + args['limit']
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#
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# let's log the sql query that has been performed here
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logger = factory.instance(type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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logger.write({"module":"bigquery","action":"read","input":{"sql":SQL}})
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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df = pd.read_gbq(SQL,credentials=credentials,dialect='standard')
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return df
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# return lambda: pd.read_gbq(SQL,credentials=credentials,dialect='standard')[args['columns']].dropna()
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@staticmethod
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def split(X,MAX_ROWS=3,PART_SIZE=3):
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return list(pd.cut( np.arange(X.shape[0]+1),PART_SIZE).categories)
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def format_schema(self,schema):
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_schema = {}
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for _item in schema :
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_type = int
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_value = 0
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if _item.field_type == 'FLOAT' :
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_type =float
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elif _item.field_type != 'INTEGER' :
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_type = str
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_value = ''
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_schema[_item.name] = _type
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return _schema
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def get_ignore(self,**_args) :
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if 'columns' in _args and 'data' in _args :
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_df = _args['data']
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terms = _args['columns']
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return [name for name in _df.columns if np.sum( [int(field in name )for field in terms ]) ]
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return []
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def set_gpu(self,**_args) :
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if 'gpu' in _args :
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gpu = _args['gpu'] if type(_args['gpu']) != str else [_args['gpu']]
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_index = str(gpu[0])
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os.environ['CUDA_VISIBLE_DEVICES'] = _index
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return gpu
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else :
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return None
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def train(self,**args):
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"""
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This function will perform training on the basis of a given pointer that reads data
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"""
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schema = None
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if 'file' in args :
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df = pd.read_csv(args['file'])
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del args['file']
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elif 'data' not in args :
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reader = factory.instance(**args['store']['source'])
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if 'row_limit' in args :
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df = reader.read(sql=args['sql'],limit=args['row_limit'])
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else:
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df = reader.read(sql=args['sql'])
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schema = reader.meta(table=args['from']) if hasattr(reader,'meta') and 'from' in args else None
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else:
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df = args['data']
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#
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#
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# df = df.fillna('')
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if schema :
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_schema = []
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for _item in schema :
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_type = int
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_value = 0
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if _item.field_type == 'FLOAT' :
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_type =float
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elif _item.field_type != 'INTEGER' :
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_type = str
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_value = ''
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_schema += [{"name":_item.name,"type":_item.field_type}]
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df[_item.name] = df[_item.name].fillna(_value).astype(_type)
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args['schema'] = _schema
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# df[_item.name] = df[_item.name].astype(_type)
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_args = copy.deepcopy(args)
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# _args['store'] = args['store']['source']
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_args['data'] = df
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#
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# The columns that are continuous should also be skipped because they don't need to be synthesied (like-that)
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if 'continuous' in args :
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x_cols = args['continuous']
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else:
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x_cols = []
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if 'ignore' in args and 'columns' in args['ignore'] :
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_cols = self.get_ignore(data=df,columns=args['ignore']['columns'])
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_args['data'] = df[ list(set(df.columns)- set(_cols))]
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#
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# We need to make sure that continuous columns are removed
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if x_cols :
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_args['data'] = _args['data'][list(set(_args['data'].columns) - set(x_cols))]
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if 'gpu' in args :
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_args['gpu'] = self.set_gpu(gpu=args['gpu'])
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if 'partition' in args :
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_args['partition'] = args['partition']
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if df.shape[0] and df.shape[0] :
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#
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# We have a full blown matrix to be processed
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print ('-- Training --')
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data.maker.train(**_args)
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else:
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print ("... skipping training !!")
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if 'autopilot' in ( list(args.keys())) :
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args['data'] = df
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print (['autopilot mode enabled ....',args['context']])
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self.generate(args)
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pass
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def approximate(self,values):
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"""
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:param values array of values to be approximated
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"""
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if values.dtype in [int,float] :
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#
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# @TODO: create bins?
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r = np.random.dirichlet(values+.001) #-- dirichlet doesn't work on values with zeros
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_sd = values[values > 0].std()
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_me = values[values > 0].mean()
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_mi = values.min()
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x = []
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_type = values.dtype
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for index in np.arange(values.size) :
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if np.random.choice([0,1],1)[0] :
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value = values[index] + (values[index] * r[index])
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else :
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value = values[index] - (values[index] * r[index])
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#
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# randomly shifting the measurements
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if np.random.choice([0,1],1)[0] and _me > _sd :
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if np.random.choice([0,1],1)[0] :
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value = value * np.divide(_me,_sd)
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else:
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value = value + (np.divide(_me,_sd))
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value = int(value) if _type == int else np.round(value,2)
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x.append( value)
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np.random.shuffle(x)
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return np.array(x)
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else:
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return values
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pass
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def shuffle(self,_args):
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if 'data' in args :
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df = data['data']
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else:
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reader = factory.instance(**args['store']['source'])
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if 'file' in args :
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df = pd.read_csv(args['file'])
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elif 'data' in _args :
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df = _args['data']
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else:
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if 'row_limit' in args and 'sql' in args:
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df = reader.read(sql=args['sql'],limit=args['row_limit'])
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else:
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df = reader.read(sql=args['sql'])
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schema = None
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if 'schema' not in args and hasattr(reader,'meta') and 'file' not in args:
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schema = reader.meta(table=args['from'])
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schema = [{"name":_item.name,"type":_item.field_type} for _item in schema]
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#
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# We are shufling designated colmns and will be approximating the others
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#
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x_cols = [] #-- coumns tobe approximated.
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_cols = [] #-- columns to be ignored
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if 'continuous' in args :
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x_cols = args['continuous']
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if 'ignore' in args and 'columns' in args['ignore'] :
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_cols = self.get_ignore(data=df,columns=args['ignore']['columns'])
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columns = args['columns'] if 'columns' in args else df.columns
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columns = list(set(columns) - set(_cols))
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for name in columns:
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i = np.arange(df.shape[0])
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np.random.shuffle(i)
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if name in x_cols :
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if df[name].unique().size > 0 :
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df[name] = self.approximate(df.iloc[i][name].fillna(0).values)
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# df[name] = df[name].astype(str)
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# pass
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df.index = np.arange(df.shape[0])
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self.post(data=df,schema=schema,store=args['store']['target'])
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def post(self,**_args) :
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table = _args['from'] if 'from' in _args else _args['store']['table']
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_schema = _args['schema'] if 'schema' in _args else None
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writer = factory.instance(**_args['store'])
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_df = _args['data']
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if _schema :
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columns = []
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for _item in _schema :
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name = _item['name']
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_type = str
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_value = 0
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if _item['type'] in ['DATE','TIMESTAMP','DATETIMESTAMP','DATETIME'] :
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if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
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#
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# There is an issue with missing dates that needs to be resolved.
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# for some reason a missing date/time here will cause the types to turn into timestamp (problem)
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# The following is a hack to address the issue (alas) assuming 10 digit dates and 'NaT' replaces missing date values (pandas specifications)
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#
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_df[name] = _df[name].apply(lambda value: None if str(value) == 'NaT' else (str(value)[:10]) if _item['type'] in ['DATE','DATETIME'] else str(value))
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#_df[name] = _df[name].dt.date
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# _df[name] = pd.to_datetime(_df[name].fillna(''),errors='coerce')
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else:
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pass
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_df[name] = pd.to_datetime(_df[name])
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else:
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value = 0
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if _item['type'] == 'INTEGER' :
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_type = np.int64
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elif _item['type'] in ['FLOAT','NUMERIC']:
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_type = np.float64
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else:
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_value = ''
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_df[name] = _df[name].fillna(_value) #.astype(_type)
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columns.append(name)
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fields = _df.columns.tolist()
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if not writer.has(table=table) and _args['store']['provider'] != 'bigquery':
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_map = {'STRING':'VARCHAR(256)','INTEGER':'BIGINT'} if 'provider' in _args['store'] and _args['store']['provider'] != 'bigquery' else {}
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_params = {'map':_map,'table':args['from']}
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if _schema :
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_params['schema'] = _schema
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else:
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_params['fields'] = fields
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writer.make(**_params)
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fields = _df.columns.tolist()
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_df = _df[fields]
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# writer.fields = fields
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if _args['store']['provider'] == 'bigquery' :
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print (['_______ POSTING ______________ ',table])
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print (['_______________ ',_df.shape[0],' ___________________'])
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writer.write(_df.astype(object),schema=_schema,table=table)
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else:
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writer.table = table
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writer.write(_df)
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# else:
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# writer.write(_df,table=args['from'])
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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 ORDER BY RAND()) 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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"""
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store = args['store']['logs']
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if 'args' in store :
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store['args']['doc'] = args['context']
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else:
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store['doc'] = args['context']
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logger = factory.instance(**store) #type='mongo.MongoWriter',args={'dbname':'aou','doc':args['context']})
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ostore = args['store']['target']
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writer = factory.instance(**ostore)
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schema = args['schema'] if 'schema' in args else None
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if 'data' in args :
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df = args['data']
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else:
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reader = factory.instance(**args['store']['source'])
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if 'row_limit' in args :
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df = reader.read(sql=args['sql'],limit=args['row_limit'])
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else:
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df = reader.read(sql=args['sql'])
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if 'schema' not in args and hasattr(reader,'meta'):
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schema = reader.meta(table=args['from'])
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schema = [{"name":_item.name,"type":_item.field_type} for _item in schema]
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# else:
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# #
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# # This will account for autopilot mode ...
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# df = args['data']
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_cast = {}
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if schema :
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for _item in schema :
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dtype = str
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name = _item['name']
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novalue = 0
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if _item['type'] in ['INTEGER','NUMERIC']:
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dtype = np.int64
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elif _item['type'] == 'FLOAT' :
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dtype = np.float64
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else:
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novalue = ''
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# _cast[schema['name']] = dtype
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df[name] = df[name].fillna(novalue).astype(dtype)
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_info = {"module":"gan-prep","action":"read","shape":{"rows":df.shape[0],"columns":df.shape[1]},"schema":schema}
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logger.write(_info)
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_dc = pd.DataFrame()
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# for mdf in df :
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args['data'] = df.copy()
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#
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# The columns that are continuous should also be skipped because they don't need to be synthesied (like-that)
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if 'continuous' in args :
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x_cols = args['continuous']
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else:
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x_cols = []
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if 'ignore' in args and 'columns' in args['ignore'] :
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_cols = self.get_ignore(data=df,columns=args['ignore']['columns'])
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args['data'] = args['data'][ list(set(df.columns)- set(_cols))]
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#
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# We need to remove the continuous columns from the data-frame
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# @TODO: Abstract this !!
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#
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real_df = pd.DataFrame()
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if x_cols :
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args['data'] = args['data'][list(set(args['data'].columns) - set(x_cols))]
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real_df = df[x_cols].copy()
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args['candidates'] = 1 if 'candidates' not in args else int(args['candidates'])
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if 'gpu' in args :
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args['gpu'] = self.set_gpu(gpu=args['gpu'])
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# if 'partition' in args :
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# args['logs'] = os.sep.join([args['logs'],str(args['partition'])])
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_info = {"module":"gan-prep","action":"prune","shape":{"rows":args['data'].shape[0],"columns":args['data'].shape[1]}}
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logger.write(_info)
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if args['data'].shape[0] > 0 and args['data'].shape[1] > 0 :
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candidates = (data.maker.generate(**args))
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else:
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candidates = [df]
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# if 'sql.BQWriter' in ostore['type'] :
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_columns = None
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skip_columns = []
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_schema = schema
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if schema :
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cols = [_item['name'] for _item in _schema]
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else:
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cols = df.columns.tolist()
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_info = {"module":"gan-prep","action":"selection","input":{"candidates":len(candidates),"features":cols}}
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logger.write(_info)
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for _df in candidates :
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#
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# we need to format the fields here to make sure we have something cohesive
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#
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if not skip_columns :
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if 'ignore' in args and 'columns' in args['ignore'] :
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skip_columns = self.get_ignore(data=_df,columns=args['ignore']['columns'])
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#
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# We perform a series of set operations to insure that the following conditions are met:
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# - the synthetic dataset only has fields that need to be synthesized
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# - The original dataset has all the fields except those that need to be synthesized
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#
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_df = _df[list(set(_df.columns) - set(skip_columns))].copy()
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if x_cols :
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_approx = {}
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for _col in x_cols :
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if real_df[_col].unique().size > 0 :
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_df[_col] = self.approximate(real_df[_col].values)
|
|
_approx[_col] = {
|
|
"io":{"min":_df[_col].min().astype(float),"max":_df[_col].max().astype(float),"mean":_df[_col].mean().astype(float),"sd":_df[_col].values.std().astype(float),"missing": _df[_col].where(_df[_col] == -1).dropna().count().astype(float),"zeros":_df[_col].where(_df[_col] == 0).dropna().count().astype(float)},
|
|
"real":{"min":real_df[_col].min().astype(float),"max":real_df[_col].max().astype(float),"mean":real_df[_col].mean().astype(float),"sd":real_df[_col].values.std().astype(float),"missing": real_df[_col].where(_df[_col] == -1).dropna().count().astype(float),"zeros":real_df[_col].where(_df[_col] == 0).dropna().count().astype(float)}
|
|
}
|
|
else:
|
|
_df[_col] = -1
|
|
logger.write({"module":"gan-generate","action":"approximate","status":_approx})
|
|
if set(df.columns) & set(_df.columns) :
|
|
_columns = list(set(df.columns) - set(_df.columns))
|
|
df = df[_columns]
|
|
|
|
#
|
|
# Let us merge the dataset here and and have a comprehensive dataset
|
|
|
|
_df = pd.DataFrame.join(df,_df)
|
|
_params = {'data':_df,'store' : ostore}
|
|
if _schema :
|
|
_params ['schema'] = _schema
|
|
_info = {"module":"gan-prep","action":"write","input":{"rows":_df.shape[0],"cols":_df.shape[1]}}
|
|
logger.write(_info)
|
|
self.post(**_params)
|
|
# print (['_______ posting _________________',_df.shape])
|
|
break
|
|
|
|
|
|
pass
|
|
# else:
|
|
# pass
|
|
def bind(self,**_args):
|
|
print (_args)
|
|
|
|
|
|
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 'matrix_size' in args :
|
|
args['matrix_size'] = int(args['matrix_size'])
|
|
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'
|
|
args['logs'] = args['logs'] if 'logs' in args else 'logs'
|
|
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 ...
|
|
#
|
|
|
|
GPU_CHIPS = args['gpu'] if 'gpu' in args else None
|
|
if GPU_CHIPS and type(GPU_CHIPS) != list :
|
|
GPU_CHIPS = [int(_id.strip()) for _id in GPU_CHIPS.split(',')] if type(GPU_CHIPS) == str else [GPU_CHIPS]
|
|
if 'gpu' in SYS_ARGS :
|
|
args['gpu'] = GPU_CHIPS
|
|
jobs = []
|
|
if 'generate' in SYS_ARGS :
|
|
#
|
|
# Let us see if we have partitions given the log folder
|
|
|
|
content = os.listdir( os.sep.join([args['logs'],'train',args['context']]))
|
|
if 'all-chips' in SYS_ARGS and GPU_CHIPS:
|
|
index = 0
|
|
jobs = []
|
|
for _gpu in GPU_CHIPS :
|
|
_args = copy.deepcopy(args)
|
|
_args['gpu'] = [int(_gpu)]
|
|
_args['partition'] = int(_gpu) #index
|
|
index += 1
|
|
make = lambda _params: (Components()).generate(_params)
|
|
job = Process(target=make,args=( dict(_args),))
|
|
job.name = 'Trainer # ' + str(index)
|
|
job.start()
|
|
jobs.append(job)
|
|
pass
|
|
else:
|
|
|
|
generator = Components()
|
|
generator.generate(args)
|
|
elif 'bind' in SYS_ARGS :
|
|
import binder
|
|
_args = _config['_map']
|
|
_args['store'] = copy.deepcopy(_config['store'])
|
|
if 'init' in SYS_ARGS :
|
|
#
|
|
# Creating and persisting the map ...
|
|
print (['.... Binding Initialization'])
|
|
# jobs = binder.Init(**_args)
|
|
_mapped = binder.Init(**_args)
|
|
|
|
|
|
_schema = [{"name":_name,"type":"INTEGER"} for _name in _mapped.columns.tolist()]
|
|
publisher = lambda _params: (Components()).post(**_params)
|
|
_args = {'data':_mapped,'store':_config['store']['target']}
|
|
_args['store']['table'] = '_map'
|
|
if _args['store']['provider'] =='bigquery' :
|
|
_args['schema'] = _schema
|
|
|
|
job = Process (target = publisher,args=(_args,))
|
|
job.start()
|
|
jobs = [job]
|
|
else:
|
|
#
|
|
# Applying the map of k on a particular dataset
|
|
#
|
|
index = int(SYS_ARGS['index'])
|
|
_args['config'] = _config['pipeline'][index]
|
|
_args['original_key'] = 'person_id' if 'original_key' in _config else 'person_id'
|
|
table = _config['pipeline'][index]['from']
|
|
_df = binder.ApplyOn(**_args)
|
|
_df = np.array_split(_df,PART_SIZE)
|
|
jobs = []
|
|
print (['Publishing ',PART_SIZE,' PARTITION'])
|
|
for data in _df :
|
|
publisher = lambda _params: ( Components() ).post(**_params)
|
|
_args = {'data':data,'store':_config['store']['target']}
|
|
_args['store']['table'] = table
|
|
print (_args['store'])
|
|
job = Process(target = publisher,args=(_args,))
|
|
job.name = "Publisher "+str(len(jobs)+1)
|
|
job.start()
|
|
jobs.append(job)
|
|
|
|
elif 'shuffle' in SYS_ARGS :
|
|
index = 0
|
|
if GPU_CHIPS and 'all-chips' in SYS_ARGS:
|
|
|
|
for index in GPU_CHIPS :
|
|
publisher = lambda _params: ( Components() ).shuffle(_params)
|
|
job = Process (target = publisher,args=( args,))
|
|
job.name = 'Shuffler #' + str(index)
|
|
job.start()
|
|
jobs.append(job)
|
|
else:
|
|
shuffler = Components()
|
|
shuffler.shuffle(args)
|
|
pass
|
|
elif 'train' in SYS_ARGS:
|
|
|
|
# DATA = np.array_split(DATA,PART_SIZE)
|
|
#
|
|
# Let us create n-jobs across n-gpus, The assumption here is the data that is produced will be a partition
|
|
# @TODO: Find better name for partition
|
|
#
|
|
|
|
if GPU_CHIPS and 'all-chips' in SYS_ARGS:
|
|
index = 0
|
|
print (['... launching ',len(GPU_CHIPS),' jobs',args['context']])
|
|
for _gpu in GPU_CHIPS :
|
|
_args = copy.deepcopy(args)
|
|
_args['gpu'] = [int(_gpu)]
|
|
_args['partition'] = int(_gpu) #index
|
|
index += 1
|
|
make = lambda _params: (Components()).train(**_params)
|
|
job = Process(target=make,args=( _args,))
|
|
job.name = 'Trainer # ' + str(index)
|
|
job.start()
|
|
jobs.append(job)
|
|
|
|
|
|
|
|
|
|
else:
|
|
#
|
|
# The choice of the chip will be made internally
|
|
|
|
agent = Components()
|
|
agent.train(**args)
|
|
#
|
|
# If we have any obs we should wait till they finish
|
|
#
|
|
DIRTY = 0
|
|
if (len(jobs)) :
|
|
print (['.... waiting on ',len(jobs),' jobs'])
|
|
while len(jobs)> 0 :
|
|
DIRTY =1
|
|
jobs = [job for job in jobs if job.is_alive()]
|
|
time.sleep(2)
|
|
if DIRTY:
|
|
print (["..:: jobs finished "])
|
|
#
|
|
# We need to harmonize the keys if any at all in this case we do this for shuffle or generate operations
|
|
# This holds true for bigquery - bigquery only
|
|
IS_BIGQUERY = _config['store']['source']['provider'] == _config['store']['target']['provider'] and _config['store']['source']['provider'] == 'bigquery'
|
|
|
|
# if 'bind' not in SYS_ARGS and IS_BIGQUERY and ('autopilot' in SYS_ARGS or 'finalize' in SYS_ARGS or ('generate' in SYS_ARGS or 'shuffle' in SYS_ARGS)) :
|
|
# #
|
|
# # We should pull all the primary keys and regenerate them in order to insure some form of consistency
|
|
# #
|
|
|
|
# #
|
|
# #
|
|
|
|
# print (["..:: Finalizing process"])
|
|
# (Components()).finalize(args)
|