old_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  # Author: Gusseppe Bravo <gbravor@uni.pe>
  # License: BSD 3 clause
  
  """
  This module provides the logic of the whole project.
  
  """
  #import define
  #import analyze
  #import prepare
  #import feature_selection
  #import evaluate
  
  #import time
  
  #name = "datasets/iris.csv"
  ##name = "datasets/miningData.csv"
  ##name = "datasets/LocalizationOld.csv"
  ##name = "datasets/seguridad.csv"
  ##name = "datasets/breast-cancer-wisconsin.csv"
  ##name = "breast-cancer-wisconsin.csv"
  ##name = "inputBus.csv"
  ## className = "Ruta"
  ##className = "CATEGORY"
  ##className = "accident_type"
  ##className = "position"
  #className = "class"
  
  #def main():
      ##STEP 0: Define workflow parameters
      #definer = define.Define(nameData=name, className=className).pipeline()
  
      ##STEP 1: Analyze data by ploting it
      ##analyze.Analyze(definer).pipeline()
  
      ##STEP 2: Prepare data by scaling, normalizing, etc.
      #preparer = prepare.Prepare(definer).pipeline()
  
      ##STEP 3: Feature selection
      #featurer = feature_selection.FeatureSelection(definer).pipeline()
  
      ##STEP4: Evalute the algorithms by using the pipelines
      ##evaluator = evaluate.Evaluate(definer, preparer, featurer).pipeline()
  
  #if __name__ == '__main__':
      #start = time.time()
      #main()
      #end = time.time()
  
      #print()
      #print("Execution time for all the steps: ", end-start)
  
  # TESTING
  import define
  import analyze
  import prepare
  import fselect
  import evaluate
  import improve
  
  data_name = "iris.csv"
  class_name = "class"
  definer = define.Define(
          data_path=data_name,
          header=None,
          response=class_name).pipeline()
  
  preparer = prepare.Prepare(definer).pipeline()
  selector = fselect.Select(definer).pipeline()
  evaluator = evaluate.Evaluate(definer, preparer, selector)
  improver = improve.Improve(evaluator).pipeline()
  # improver.improve_pipelines()
  print(improver.score_report)
  print(improver.full_report)
  
new_contents: #!/usr/bin/python
  # -*- coding: utf-8 -*-
  
  # Author: Gusseppe Bravo <gbravor@uni.pe>
  # License: BSD 3 clause
  
  """
  This module provides the logic of the whole project.
  
  """
  #import define
  #import analyze
  #import prepare
  #import feature_selection
  #import evaluate
  
  #import time
  
  #name = "datasets/iris.csv"
  ##name = "datasets/miningData.csv"
  ##name = "datasets/LocalizationOld.csv"
  ##name = "datasets/seguridad.csv"
  ##name = "datasets/breast-cancer-wisconsin.csv"
  ##name = "breast-cancer-wisconsin.csv"
  ##name = "inputBus.csv"
  ## className = "Ruta"
  ##className = "CATEGORY"
  ##className = "accident_type"
  ##className = "position"
  #className = "class"
  
  #def main():
      ##STEP 0: Define workflow parameters
      #definer = define.Define(nameData=name, className=className).pipeline()
  
      ##STEP 1: Analyze data by ploting it
      ##analyze.Analyze(definer).pipeline()
  
      ##STEP 2: Prepare data by scaling, normalizing, etc.
      #preparer = prepare.Prepare(definer).pipeline()
  
      ##STEP 3: Feature selection
      #featurer = feature_selection.FeatureSelection(definer).pipeline()
  
      ##STEP4: Evalute the algorithms by using the pipelines
      ##evaluator = evaluate.Evaluate(definer, preparer, featurer).pipeline()
  
  #if __name__ == '__main__':
      #start = time.time()
      #main()
      #end = time.time()
  
      #print()
      #print("Execution time for all the steps: ", end-start)
  
  # TESTING
  # import define
  # import analyze
  # import prepare
  # import fselect
  # import evaluate
  # import improve
  #
  # data_name = "iris.csv"
  # class_name = "class"
  # definer = define.Define(
  #         data_path=data_name,
  #         header=None,
  #         response=class_name).pipeline()
  #
  # preparer = prepare.Prepare(definer).pipeline()
  # selector = fselect.Select(definer).pipeline()
  # evaluator = evaluate.Evaluate(definer, preparer, selector)
  # improver = improve.Improve(evaluator).pipeline()
  # # improver.improve_pipelines()
  # print(improver.score_report)
  # print(improver.full_report)
  
subject: Improve hyper