fix: table schema (urgh)
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pipeline.py
63
pipeline.py
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@ -7,6 +7,7 @@ import os
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from multiprocessing import Process, Lock
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from multiprocessing import Process, Lock
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import pandas as pd
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import pandas as pd
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from google.oauth2 import service_account
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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 data.maker
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from data.params import SYS_ARGS
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from data.params import SYS_ARGS
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@ -115,11 +116,45 @@ class Components :
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data.maker.train(**_args)
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data.maker.train(**_args)
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if 'autopilot' in ( list(args.keys())) :
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if 'autopilot' in ( list(args.keys())) :
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print (['drone mode enabled ....'])
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print (['autopilot mode enabled ....'])
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self.generate(args)
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self.generate(args)
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pass
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pass
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def shuffle(self,args):
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"""
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"""
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df = args['reader']() if 'reader' in args else args['data']
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col = args['columns'][0]
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distrib = df[col].value_counts()
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values = np.array(distrib.index)
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counts = np.array(distrib.values)
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np.random.shuffle(values)
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np.random.shuffle(counts)
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N = len (values)
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theta = np.random.sample()
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pad = 0
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# print (values)
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iovalues = np.zeros(df.shape[0],dtype=df[col].dtype)
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for i in range(N) :
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# n = int(counts[i] - counts[i]*theta)
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n = counts[i]
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print ([counts[i],theta,n])
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index = np.where(iovalues == 0)[0]
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if index.size > 0 and index.size > n:
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index = index[:n]
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iovalues[index] = values[i]
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np.random.shuffle(iovalues)
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df[col] = iovalues
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return df
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def post(self,args):
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pass
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# @staticmethod
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# @staticmethod
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def generate(self,args):
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def generate(self,args):
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"""
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"""
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@ -181,12 +216,12 @@ class Components :
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# let us fix the data types here every _id field will be an np.int64...
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# let us fix the data types here every _id field will be an np.int64...
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#
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#
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for name in df.columns.tolist():
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# for name in df.columns.tolist():
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if name.endswith('_id') :
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# if name.endswith('_id') :
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if df[name].isnull().sum() > 0 :
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# if df[name].isnull().sum() > 0 and name not in ['unique_device_id']:
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df[name].fillna(np.nan_to_num(np.nan),inplace=True)
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# df[name].fillna(np.nan_to_num(np.nan),inplace=True)
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df[name] = df[name].astype(int)
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# df[name] = df[name].astype(int)
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_dc = pd.DataFrame()
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_dc = pd.DataFrame()
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@ -232,6 +267,11 @@ class Components :
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_id = 'path'
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_id = 'path'
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else:
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else:
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client = bq.Client.from_service_account_json(args["private_key"])
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full_schema = client.get_table(client.dataset(args['dataset']).table(args['from'])).schema
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full_schema = [{'name':item.name,'type':item.field_type,'description':item.description} for item in full_schema]
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io_schema = [{'name':item['name'],'type':item['type'],'description':item['description']} for item in full_schema if item['name'] in args['columns']]
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credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
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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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_pname = os.sep.join([folder,table+'.csv'])
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_fname = table.replace('_io','_full_io')
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_fname = table.replace('_io','_full_io')
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@ -243,11 +283,11 @@ class Components :
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else:
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else:
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Components.lock.acquire()
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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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data_comp.to_gbq(if_exists='append',destination_table=partial,credentials=credentials,chunksize=90000,table_schema=io_schema)
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INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append'
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INSERT_FLAG = 'replace' if 'partition' not in args or 'segment' not in args else 'append'
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_args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000)
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_args['data'].to_gbq(if_exists='append',destination_table=complete,credentials=credentials,chunksize=90000,table_schema=full_schema)
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Components.lock.release()
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Components.lock.release()
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_id = 'dataset'
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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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info = {"full":{_id:_fname,"rows":_args['data'].shape[0]},"partial":{"path":_pname,"rows":data_comp.shape[0]} }
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@ -354,7 +394,12 @@ if __name__ == '__main__' :
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else:
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else:
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generator.generate(args)
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generator.generate(args)
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# Components.generate(args)
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# Components.generate(args)
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elif 'shuffle' in SYS_ARGS:
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args['data'] = DATA[0]
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_df = (Components()).shuffle(args)
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print (DATA[0][args['columns']])
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print ()
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print (_df[args['columns']])
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else:
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else:
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# DATA = np.array_split(DATA,PART_SIZE)
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# DATA = np.array_split(DATA,PART_SIZE)
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