data-maker/finalize.py

182 lines
6.5 KiB
Python

#!/usr/bin/env python3
"""
This file will perform basic tasks to finalize the GAN process by performing the following :
- basic stats & analytics
- rebuild io to another dataset
"""
import pandas as pd
import numpy as np
from google.oauth2 import service_account
from google.cloud import bigquery as bq
from data.params import SYS_ARGS
import json
class Analytics :
"""
This class will compile basic analytics about a given dataset i.e compare original/synthetic
"""
@staticmethod
def distribution(**args):
context = args['context']
df = args['data']
#
#-- This data frame counts unique values for each feature (space)
df_counts = pd.DataFrame(df.apply(lambda col: col.unique().size),columns=['counts']).T # unique counts
#
#-- Get the distributions for common values
#
names = [name for name in df_counts.columns.tolist() if name.endswith('_io') == False]
ddf = df.apply(lambda col: pd.DataFrame(col.values,columns=[col.name]).groupby([col.name]).size() ).fillna(0)
ddf[context] = ddf.index
pass
def distance(**args):
"""
This function will measure the distance between
"""
df = args['data']
names = [name for name in df_counts.columns.tolist() if name.endswith('_io') == False]
class Utils :
class get :
@staticmethod
def config(**args) :
contexts = args['contexts'].split(',') if type(args['contexts']) == str else args['contexts']
pipeline = args['pipeline']
return [ item for item in pipeline if item['context'] in contexts]
@staticmethod
def sql(**args) :
"""
This function is intended to build SQL query for the remainder of the table that was not synthesized
:config configuration entries
:from source of the table name
:dataset name of the source dataset
"""
SQL = ["SELECT * FROM :from "]
SQL_FILTER = []
NO_FILTERS_FOUND = True
pipeline = Utils.get.config(**args)
REVERSE_QUALIFIER = {'IN':'NOT IN','NOT IN':'IN','=':'<>','<>':'='}
for item in pipeline :
if 'filter' in item :
if NO_FILTERS_FOUND :
NO_FILTERS_FOUND = False
SQL += ['WHERE']
#
# Let us load the filter in the SQL Query
FILTER = item['filter']
QUALIFIER = REVERSE_QUALIFIER[FILTER['qualifier'].upper()]
SQL_FILTER += [" ".join([FILTER['field'], QUALIFIER,'(',FILTER['value'],')']).replace(":dataset",args['dataset'])]
src = ".".join([args['dataset'],args['from']])
SQL += [" AND ".join(SQL_FILTER)]
#
# let's pull the field schemas out of the table definition
#
return " ".join(SQL).replace(":from",src)
def mk(**args) :
dataset = args['dataset']
client = args['client'] if 'client' in args else bq.Client.from_service_account_file(args['private_key'])
#
# let us see if we have a dataset handy here
#
datasets = list(client.list_datasets())
found = [item for item in datasets if item.dataset_id == dataset]
if not found :
return client.create_dataset(dataset)
return found[0]
def move (**args):
"""
This function will move a table from the synthetic dataset into a designated location
This is the simplest case for finalizing a synthetic data set
:private_key
"""
private_key = args['private_key']
client = bq.Client.from_service_account_json(private_key)
config = Utils.get.config(**args)
dataset = args['dataset']
if 'contexts' in args :
SQL = [ ''.join(["SELECT * FROM io.",item['context'],'_full_io']) for item in config]
SQL += [Utils.get.sql(**args)]
SQL = ('\n UNION ALL \n'.join(SQL).replace(':dataset','io'))
else:
#
# moving a table to a designated location
tablename = args['from']
SQL = "SELECT * FROM :dataset.:table".replace(":dataset",dataset).replace(":table",tablename)
#
# At this point we have gathered all the tables in the io folder and we should now see if we need to merge with the remainder from the original table
#
odataset = mk(dataset=dataset+'_io',client=client)
# SQL = "SELECT * FROM io.:context_full_io".replace(':context',context)
config = bq.QueryJobConfig()
config.destination = client.dataset(odataset.dataset_id).table(args['from'])
config.use_query_cache = True
config.allow_large_results = True
config.priority = 'INTERACTIVE'
#
#
schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
fields = [" ".join(["CAST (",item.name,"AS",item.field_type.replace("INTEGER","INT64").replace("FLOAT","FLOAT64"),") ",item.name]) for item in schema]
SQL = SQL.replace("*"," , ".join(fields))
# print (SQL)
out = client.query(SQL,location='US',job_config=config)
print ()
return (out.job_id)
import pandas as pd
import numpy as np
from google.oauth2 import service_account
import json
# path = '../curation-prod.json'
# credentials = service_account.Credentials.from_service_account_file(path)
# df = pd.read_gbq("SELECT * FROM io.icd10_partial_io",credentials=credentials,dialect='standard')
filename = 'config.json' if 'config' not in SYS_ARGS else SYS_ARGS['config']
f = open(filename)
config = json.loads(f.read())
args = config['pipeline']
f.close()
if __name__ == '__main__' :
"""
Usage :
finalize --<move|stats> --contexts <c1,c2,...c3> --from <table>
"""
if 'move' in SYS_ARGS :
# table = SYS_ARGS['from']
# args = dict(config,**{"private_key":"../curation-prod.json"})
args = dict(args,**SYS_ARGS)
contexts = [item['context'] for item in config['pipeline'] if item['from'] == SYS_ARGS['from']]
log = []
if contexts :
args['contexts'] = contexts
log = move(**args)
else:
tables = args['from'].split(',')
for name in tables :
name = name.strip()
args['from'] = name
log += [move(**args)]
print ("\n".join(log))
else:
print ("NOT YET READY !")