512 lines
16 KiB
Python
512 lines
16 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 ]) > 0 ]
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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 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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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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r = np.random.dirichlet(values)
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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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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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# @staticmethod
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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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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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_info = {"module":"gan-prep","action":"read","shape":{"rows":df.shape[0],"columns":df.shape[0]}}
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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['data'][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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candidates = (data.maker.generate(**args))
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if 'sql.BQWriter' in ostore['type'] :
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#table = ".".join([ostore['['dataset'],args['context']])
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# writer = factory.instance(**ostore)
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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
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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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# _columns = set(df.columns) - set(_df.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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# for name in args['ignore']['columns'] :
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# for _name in _df.columns:
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# if _name in name:
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# skip_columns.append(_name)
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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))]
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if x_cols :
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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(df[_col].fillna(-1))
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else:
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_df[_col] = -1
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if set(df.columns) & set(_df.columns) :
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_columns = set(df.columns) - set(_df.columns)
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df = df[_columns]
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#
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# Let us merge the dataset here and and have a comprehensive dataset
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_df = pd.DataFrame.join(df,_df)
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if _schema :
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for _item in _schema :
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if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
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_df[_item['name']] = _df[_item['name']].astype(str)
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pass
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if _schema :
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writer.write(_df[cols],schema=_schema,table=args['from'])
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else:
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writer.write(_df[cols],table=args['from'])
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# writer.write(df,table=table)
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pass
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else:
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pass
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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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# 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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# #
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# # performing basic analytics on the synthetic data generated (easy to quickly asses)
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# #
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# info = {"module":"generate","action":"io.metrics","input":{"rows":data_comp.shape[0],"partition":partition,"logs":[]}}
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# #
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# # @TODO: Send data over to a process for analytics
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# base_cols = list(set(_args['data'].columns) - set(args['columns'])) #-- rebuilt the dataset (and store it)
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# cols = _dc.columns.tolist()
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# for name in cols :
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# _args['data'][name] = _dc[name]
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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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# partition = str(partition)
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# table = '_'.join([prefix,partition,'io']).replace('__','_')
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# folder = os.sep.join([args['logs'],args['context'],partition,'output'])
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# if 'file' in args :
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# _fname = os.sep.join([folder,table.replace('_io','_full_io.csv')])
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# _pname = os.sep.join([folder,table])+'.csv'
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# data_comp.to_csv( _pname,index=False)
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# _args['data'].to_csv(_fname,index=False)
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# _id = 'path'
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# 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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# _pname = os.sep.join([folder,table+'.csv'])
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# _fname = table.replace('_io','_full_io')
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# partial = '.'.join(['io',args['context']+'_partial_io'])
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# complete= '.'.join(['io',args['context']+'_full_io'])
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# data_comp.to_csv(_pname,index=False)
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# if 'dump' in args :
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# print (_args['data'].head())
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# else:
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# Components.lock.acquire()
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# data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000)
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# _args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000)
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# Components.lock.release()
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# _id = 'dataset'
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# info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
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# if partition :
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# info ['partition'] = int(partition)
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# logger.write({"module":"generate","action":"write","input":info} )
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if __name__ == '__main__' :
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filename = SYS_ARGS['config'] if 'config' in SYS_ARGS else 'config.json'
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f = open (filename)
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_config = json.loads(f.read())
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f.close()
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PIPELINE = _config['pipeline']
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index = SYS_ARGS['index']
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if index.isnumeric() :
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index = int(SYS_ARGS['index'])
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else:
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#
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# The index provided is a key to a pipeline entry mainly the context
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#
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N = len(PIPELINE)
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f = [i for i in range(0,N) if PIPELINE[i]['context'] == index]
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index = f[0] if f else 0
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#
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print ("..::: ",PIPELINE[index]['context'])
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args = (PIPELINE[index])
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for key in _config :
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if key == 'pipeline' or key in args:
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#
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# skip in case of pipeline or if key exists in the selected pipeline (provided by index)
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#
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continue
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args[key] = _config[key]
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args = dict(args,**SYS_ARGS)
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if 'matrix_size' in args :
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args['matrix_size'] = int(args['matrix_size'])
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if 'batch_size' not in args :
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args['batch_size'] = 2000 #if 'batch_size' not in args else int(args['batch_size'])
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if 'dataset' not in args :
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args['dataset'] = 'combined20191004v2_deid'
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PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
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#
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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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if 'generate' in SYS_ARGS :
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#
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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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generator = Components()
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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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generator.generate(args)
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else:
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# DATA = np.array_split(DATA,PART_SIZE)
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agent = Components()
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agent.train(**args)
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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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# continue
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# args['part_size'] = PART_SIZE
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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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|
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# make = lambda _args: (Components()).train(**_args)
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# job = Process(target=make,args=( dict(args),))
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# job.name = 'Trainer # ' + str(index)
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# job.start()
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# jobs.append(job)
|
|
# # args['gpu']
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# 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)
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|
|
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# trainer = Components()
|
|
# trainer.train(**args)
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|
|
|
|
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# 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)
|