old_contents: """
  EXAMPLE SCRIPT
  Creating the Server object (for handling project directories, machine learning models, and data handling),
  creating the folder hierarchy for your project (project name, number of build sets, and nodes/set, specified in config),
  importing data from database (specified in config), fitting X machine learning models (where X is number of trials/node,
  specified in config), selecting the best performing trial from each node for the final build set, testing for multiple 
  errors, predicting values for the database and outputting them to an output file.
  """
  import ecnet
  
  server = ecnet.server.Server()			# Create server object
  server.create_save_env()				# Create a folder structure for your project
  server.import_data()					# Import data
  server.create_mlp_model()				# Create a multilayer perceptron (neural network)
  server.fit_mlp_model_validation()		# Fits the mlp using the input database (with a validation set)
  server.select_best()					# Select best trial from each build node
  
  rmse = server.test_model_rmse()			# Root-mean-square error
  mae = server.test_model_mae()			# Mean average error
  r2 = server.test_model_r2()				# Coeffecient of determination (r-squared)
  
  results = server.use_mlp_model_all()	# Calculate results from data (all values, not just test set)
  server.output_results(results, "all", "results_output.csv")		# Output results to specified file
  
  print(rmse)
  print(mae)
  print(r2)
  
new_contents: """
  EXAMPLE SCRIPT
  Creating the Server object (for handling project directories, machine learning models, and data handling),
  creating the folder hierarchy for your project (project name, number of build sets, and nodes/set, specified in config),
  importing data from database (specified in config), fitting X machine learning models (where X is number of trials/node,
  specified in config), selecting the best performing trial from each node for the final build set, testing for multiple 
  errors on your database, predicting values for the database and outputting them to an output file.
  """
  from ecnet.server import Server
  
  sv = Server()							# Create server object
  sv.create_save_env()					# Create a folder structure for your project
  sv.import_data()						# Import data
  sv.create_mlp_model()					# Create a multilayer perceptron (neural network)
  sv.fit_mlp_model_validation()			# Fits the mlp using input database (w/ validation set)
  sv.select_best()						# Select best trial from each build node
  
  results = sv.use_mlp_model_all()					# Calculate results from data (all sets)
  sv.output_results(results, "all", "cn_results.csv")	# Output results to specified file
  
  errors = sv.calc_error('rmse','r2','mean_abs_error','med_abs_error')	# Calculates errors for dataset predictions
  print(errors)
  
subject: update to syntax