...

pipeline.py

@@ -63,6 +63,24 @@ class Components :
 	def split(X,MAX_ROWS=3,PART_SIZE=3):
 		
 		return list(pd.cut( np.arange(X.shape[0]+1),PART_SIZE).categories)
+	def format_schema(self,schema):
+		_schema = {}
+		for _item in schema :                
+			_type = int
+			_value = 0
+			if _item.field_type == 'FLOAT' :
+				_type =float
+			elif _item.field_type != 'INTEGER' :
+				_type = str
+				_value = ''
+			_schema[_item.name] = _type	
+		return _schema	
+	def get_ignore(self,**_args) :
+		if 'columns' in _args and 'data' in _args :
+			_df = _args['data']
+			terms = _args['columns']
+			return [name for name in _df.columns if name in terms]
+		return []
 
 	def train(self,**args):
 		"""
@@ -84,10 +102,14 @@ class Components :
 		else:
 			df = args['data']
 		
-			
+		#
+		#
+		if 'ignore' in args and 'columns' in args['ignore'] :
+			_cols = self.get_ignore(data=df,columns=args['ignore']['columns'])
+			df = df[ list(set(df.columns)- set(_cols))]
 		# df = df.fillna('')
 		if schema :
-			_schema = {}
+			_schema = []
 			for _item in schema :                
 				_type = int
 				_value = 0
@@ -96,7 +118,7 @@ class Components :
 				elif _item.field_type != 'INTEGER' :
 					_type = str
 					_value = ''
-				_schema[_item.name] = _type
+				_schema += [{"name":_item.name,"type":_item.field_type}]
 				df[_item.name] = df[_item.name].fillna(_value).astype(_type)
 			args['schema'] = _schema
 		# 		df[_item.name] = df[_item.name].astype(_type)
@@ -107,6 +129,8 @@ class Components :
 		data.maker.train(**_args)
 		
 		if 'autopilot' in ( list(args.keys())) :
+
+			args['data'] = df
 			print (['autopilot mode enabled ....',args['context']])
 			self.generate(args)
 
@@ -127,52 +151,27 @@ class Components :
 
 		ostore = args['store']['target']
 		writer = factory.instance(**ostore)
-		# log_folder = args['logs'] if 'logs' in args else 'logs'
-		# partition = args['partition'] if 'partition' in args else ''
-		# log_folder = os.sep.join([log_folder,args['context'],str(partition)])
 
-		# _args = {"batch_size":2000,"logs":log_folder,"context":args['context'],"max_epochs":150,"column":args['columns'],"id":"person_id","logger":logger}
-		# _args['max_epochs'] = 150 if 'max_epochs' not in args else int(args['max_epochs'])
-		# _args['num_gpu'] = int(args['num_gpu']) if 'num_gpu' in args else 1 
-		# if 'batch_size' in args :
-		# 	_args['batch_size'] = int(args['batch_size'])
-		
-		# if int(args['num_gpu']) > 1 :
-		# 	_args['gpu'] = int(args['gpu']) if int(args['gpu']) < 8 else np.random.choice(np.arange(8)).astype(int)
-		# else:
-		# 	_args['gpu']  = 0
-		# _args['num_gpu'] 	= 1
-		# os.environ['CUDA_VISIBLE_DEVICES'] = str(args['gpu']) 
-		# # _args['no_value']= args['no_value']
-		# _args['matrix_size'] = args['matrix_size'] if 'matrix_size' in args else 128
-			
-		
-		# # MAX_ROWS = args['max_rows']  	if 'max_rows' in args else 0
-		# PART_SIZE = int(args['part_size']) if 'part_size' in args else 8
-		
-		# credentials = service_account.Credentials.from_service_account_file('/home/steve/dev/aou/accounts/curation-prod.json')
-		# _args['data'] = pd.read_gbq(SQL,credentials=credentials,dialect='standard').dropna()
-		# reader = args['reader']
-		# df = reader()
 		schema = args['schema'] if 'schema' in args else None
-		if 'file' in args :
+		if 'data' in args :
 			
-			df = pd.read_csv(args['file'])
+			df = args['data']
 		else:
-			if 'data' not in args :
-				reader = factory.instance(**args['store']['source'])
-				if 'row_limit' in args :
-					df = reader.read(sql=args['sql'],limit=args['row_limit'])
-				else:
-					df = reader.read(sql=args['sql'])
-				if 'schema' not in args and hasattr(reader,'meta'):
-					schema = reader.meta(table=args['from'])
-
 
+			reader = factory.instance(**args['store']['source'])
+			if 'row_limit' in args :
+				df = reader.read(sql=args['sql'],limit=args['row_limit'])
 			else:
-				#
-				# This will account for autopilot mode ...
-				df = args['data']
+				df = reader.read(sql=args['sql'])
+			if 'schema' not in args and hasattr(reader,'meta'):
+				schema = reader.meta(table=args['from'])
+				schema = [{"name":_item.name,"type":_item.field_type} for _item in schema]
+
+					
+			# else:
+			# 	#
+			# 	# This will account for autopilot mode ...
+			# 	df = args['data']
 	
 		_info = {"module":"gan-prep","action":"read","shape":{"rows":df.shape[0],"columns":df.shape[0]}}
 		
@@ -188,7 +187,7 @@ class Components :
 			# writer  = factory.instance(**ostore)
 			_columns = None
 			skip_columns = []
-			_schema = [{"name":field.name,"type":field.field_type,"description":field.description} for field in schema] if schema else []
+			_schema = schema
 			for _df in candidates :
 				#
 				# we need to format the fields here to make sure we have something cohesive
@@ -197,11 +196,11 @@ class Components :
 				if not skip_columns :
 					# _columns = set(df.columns) - set(_df.columns)
 					if 'ignore' in args and 'columns' in args['ignore'] :
-							
-							for name in args['ignore']['columns'] :
-								for _name in _df.columns:
-									if _name in name:
-										skip_columns.append(_name)
+							skip_columns = self.get_ignore(data=_df,columns=args['ignore']['columns'])
+							# for name in args['ignore']['columns'] :
+							# 	for _name in _df.columns:
+							# 		if _name in name:
+							# 			skip_columns.append(_name)
 				#
 				# We perform a series of set operations to insure that the following conditions are met:
 				#	- the synthetic dataset only has fields that need to be synthesized