adding shuffle feature to be used for very large spaces

pipeline.py

@@ -198,6 +198,52 @@ class Components :
 			return values
 		pass
 			
+	def shuffle(self,_args):
+		if 'data' in args :
+			df = data['data'] 
+		else:
+			reader = factory.instance(**args['store']['source'])
+			if 'file' in args :
+				df = pd.read_csv(args['file'])
+			else:
+				if 'row_limit' in args and 'sql' in args:
+					df = reader.read(sql=args['sql'],limit=args['row_limit'])
+				else:
+					df = reader.read(sql=args['sql'])
+			schema = None
+			if 'schema' not in args and hasattr(reader,'meta') and 'file' not in args:
+				schema = reader.meta(table=args['from'])
+				schema = [{"name":_item.name,"type":_item.field_type} for _item in schema]
+		#
+		# We are shufling designated colmns and will be approximating the others
+		#
+		x_cols 	= []	#-- coumns tobe approximated.
+		_cols 	= []	#-- columns to be ignored
+		if 'continuous' in args :
+			x_cols = args['continuous']
+		if 'ignore' in args and 'columns' in args['ignore'] :
+			_cols = self.get_ignore(data=df,columns=args['ignore']['columns'])
+		
+
+		for name in list (set(df.columns) - set(_cols)) :
+			i = np.arange(df.shape[0])
+			np.random.shuffle(i)
+			if name in x_cols :
+				df[name] = self.approximate(df[name].values)
+			df[name] = df.iloc[i][name]
+		self.post(data=df,schema=schema,store=args['store']['target'])	
+	def post(self,**_args) :
+		_schema = _args['schema'] if 'schema' in _args else None
+		writer 	= factory.instance(**_args['store'])
+		_df 	= _args['data']
+		if _schema :
+			
+			for _item in _schema :
+				if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
+					_df[_item['name']] = _df[_item['name']].astype(str)			
+			writer.write(_df,schema=_schema,table=args['from'])
+		else:
+			writer.write(_df,table=args['from'])
 
 	# @staticmethod
 	def generate(self,args):
@@ -338,20 +384,25 @@ class Components :
 
 				_df = pd.DataFrame.join(df,_df)
 				
-				if _schema :
-					for _item in _schema :
-						if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
-							_df[_item['name']] = _df[_item['name']].astype(str)
+				# if _schema :
+				# 	for _item in _schema :
+				# 		if _item['type'] in ['DATE','TIMESTAMP','DATETIME'] :
+				# 			_df[_item['name']] = _df[_item['name']].astype(str)
 				
-						pass
+				# 		pass
+				_params = {'data':_df,'store' : ostore}
 				if _schema :
-					writer.write(_df[cols],schema=_schema,table=args['from'])
-				else:
-					writer.write(_df[cols],table=args['from'])
-			# 	writer.write(df,table=table)
-			pass
-		else:
+					_params ['schema'] = _schema
+				self.post(**_params)
+				# if _schema :
+				# 	writer.write(_df[cols],schema=_schema,table=args['from'])
+				# 	self.post(data=_df,schema=)
+				# else:
+				# 	writer.write(_df[cols],table=args['from'])
+			
 			pass
+		# else:
+		# 	pass
 
 		
 		# #
@@ -537,7 +588,20 @@ if __name__ == '__main__' :
 		else:
 			generator = Components()
 			generator.generate(args)
-	
+	elif 'shuffle' in SYS_ARGS :
+		index = 0
+		if GPU_CHIPS and '--all-chips':
+
+			for index in GPU_CHIPS :
+				publisher = lambda _params: ( Components() ).shuffle(_params)
+				job = Process (target = publisher,args=( dict(args)))
+				job.name = 'Shuffler #' + str(index)
+				job.start()
+				jobs.append(job)
+		else:
+			shuffler = Components()
+			shuffler.shuffle(args)
+		pass
 	else:
 		
 		# DATA  = np.array_split(DATA,PART_SIZE)

setup.py

@@ -5,7 +5,7 @@ import sys
 def read(fname):
     return open(os.path.join(os.path.dirname(__file__), fname)).read() 
 args = {"name":"data-maker",
-        "version":"1.4.4",
+        "version":"1.4.5",
         "author":"Vanderbilt University Medical Center","author_email":"steve.l.nyemba@vanderbilt.edu","license":"MIT",
         "packages":find_packages(),"keywords":["healthcare","data","transport","protocol"]}
 args["install_requires"] = ['data-transport@git+https://dev.the-phi.com/git/steve/data-transport.git','tensorflow==1.15','pandas','pandas-gbq','pymongo']