Bug fixes, enhancements

• ecnet.Server.remove_outliers and ecnet.tasks.remove_outliers have been removed
  • while detecting outliers may be beneficial in determining abnormalities in data, removing them entirely is likely not the right approach (in terms of fuel property prediction). Once a viable usage has been determined, outlier detection will be included.
• Added the batch_size hyper-parameter, included in the default model configuration and hyper-parameter tuning process
  • Relevant unit tests updated
• Any missing model configuration variables from config files generated with previous versions of ECNet will now be set to their default values
  • Additional unit tests added
• Added option to convert SMILES to MDL during PaDEL-based database creation
  • Additional unit test added
• Added PaDEL-generated databases for all properties
• ecnet.tasks.limit_inputs.limit_rforest now relies on sklearn.ensemble.RandomForestRegressor as its only dependency
  • limit_rforest now returns list of parameter names/importances instead of a modified DataFrame
  • Server.limit_inputs also returns a list of parameter names/importances
  • Removed the ditto-lib dependency
• Bug fixes:
  • Server._sets now loads when a PRJ file is opened via ecnet.Server
  • ecnet.utils.data_utils.DataFrame.set_inputs now immediately applies selected inputs to L/V/T sets
  • ParityPlot parity lines now scale to reflect data minimum/maximum
• More robust unit tests for MultilayerPerceptron, database creation, input parameter limiting
• All unit tests may now be run individually