fix: table schema (urgh)

dev
Steve L. Nyemba 5 years ago
parent 9fff0d123e
commit f9da0f1ce7

@ -7,6 +7,7 @@ import os
from multiprocessing import Process, Lock from multiprocessing import Process, Lock
import pandas as pd import pandas as pd
from google.oauth2 import service_account from google.oauth2 import service_account
from google.cloud import bigquery as bq
import data.maker import data.maker
from data.params import SYS_ARGS from data.params import SYS_ARGS
@ -115,11 +116,45 @@ class Components :
data.maker.train(**_args) data.maker.train(**_args)
if 'autopilot' in ( list(args.keys())) : if 'autopilot' in ( list(args.keys())) :
print (['drone mode enabled ....']) print (['autopilot mode enabled ....'])
self.generate(args) self.generate(args)
pass pass
def shuffle(self,args):
"""
"""
df = args['reader']() if 'reader' in args else args['data']
col = args['columns'][0]
distrib = df[col].value_counts()
values = np.array(distrib.index)
counts = np.array(distrib.values)
np.random.shuffle(values)
np.random.shuffle(counts)
N = len (values)
theta = np.random.sample()
pad = 0
# print (values)
iovalues = np.zeros(df.shape[0],dtype=df[col].dtype)
for i in range(N) :
# n = int(counts[i] - counts[i]*theta)
n = counts[i]
print ([counts[i],theta,n])
index = np.where(iovalues == 0)[0]
if index.size > 0 and index.size > n:
index = index[:n]
iovalues[index] = values[i]
np.random.shuffle(iovalues)
df[col] = iovalues
return df
def post(self,args):
pass
# @staticmethod # @staticmethod
def generate(self,args): def generate(self,args):
""" """
@ -181,12 +216,12 @@ class Components :
# let us fix the data types here every _id field will be an np.int64... # let us fix the data types here every _id field will be an np.int64...
# #
for name in df.columns.tolist(): # for name in df.columns.tolist():
if name.endswith('_id') : # if name.endswith('_id') :
if df[name].isnull().sum() > 0 : # if df[name].isnull().sum() > 0 and name not in ['unique_device_id']:
df[name].fillna(np.nan_to_num(np.nan),inplace=True) # df[name].fillna(np.nan_to_num(np.nan),inplace=True)
df[name] = df[name].astype(int) # df[name] = df[name].astype(int)
_dc = pd.DataFrame() _dc = pd.DataFrame()
@ -232,6 +267,11 @@ class Components :
_id = 'path' _id = 'path'
else: else:
client = bq.Client.from_service_account_json(args["private_key"])
full_schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
full_schema = [{'name':item.name,'type':item.field_type,'description':item.description} for item in full_schema]
io_schema = [{'name':item['name'],'type':item['type'],'description':item['description']} for item in full_schema if item['name'] in args['columns']]
credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json') credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
_pname = os.sep.join([folder,table+'.csv']) _pname = os.sep.join([folder,table+'.csv'])
_fname = table.replace('_io','_full_io') _fname = table.replace('_io','_full_io')
@ -243,11 +283,11 @@ class Components :
else: else:
Components.lock.acquire() Components.lock.acquire()
data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000) data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000,table_schema=io_schema)
INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append' INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append'
_args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000) _args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000,table_schema=full_schema)
Components.lock.release() Components.lock.release()
_id = 'dataset' _id = 'dataset'
info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} } info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
@ -354,7 +394,12 @@ if __name__ == '__main__' :
else: else:
generator.generate(args) generator.generate(args)
# Components.generate(args) # Components.generate(args)
elif 'shuffle' in SYS_ARGS:
args['data'] = DATA[0]
_df = (Components()).shuffle(args)
print (DATA[0][args['columns']])
print ()
print (_df[args['columns']])
else: else:
# DATA = np.array_split(DATA,PART_SIZE) # DATA = np.array_split(DATA,PART_SIZE)

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