old_contents: '''
  Created on 30 Oct 2018
  
  @author: jhkwakkel
  '''
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  import seaborn as sns
  
  from ema_workbench import ema_logging, load_results
  from ema_workbench.analysis.feature_scoring import (get_ex_feature_scores,
                                                      RuleInductionType)
  
  ema_logging.log_to_stderr(level=ema_logging.INFO)
  
  # load data
  fn = r'./data/1000 flu cases no policy.tar.gz'
  x, outcomes = load_results(fn)
  
  x = x.drop(['model', 'policy'], axis=1)
  y = np.max(outcomes['infected fraction R1'], axis=1)
  
  
  all_scores = []
  for i in range(100):
      scores = get_ex_feature_scores(x, y,
                                     mode=RuleInductionType.REGRESSION)[0]
      all_scores.append(scores)
  all_scores = pd.concat(all_scores, axis=1, sort=False)
  
  sns.boxplot(data=all_scores.T)
  plt.show()
  
new_contents: '''
  Created on 30 Oct 2018
  
  @author: jhkwakkel
  '''
  import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  import seaborn as sns
  
  from ema_workbench import ema_logging, load_results
  from ema_workbench.analysis.feature_scoring import (get_ex_feature_scores,
                                                      RuleInductionType)
  
  ema_logging.log_to_stderr(level=ema_logging.INFO)
  
  # load data
  fn = r'./data/1000 flu cases no policy.tar.gz'
  x, outcomes = load_results(fn)
  
  x = x.drop(['model', 'policy'], axis=1)
  y = np.max(outcomes['infected fraction R1'], axis=1)
  
  
  all_scores = []
  for i in range(100):
      indices = np.random.choice(np.arange(0, x.shape[0]), size=x.shape[0])
      selected_x = x.iloc[indices, :]
      selected_y = y[indices]
      
      scores = get_ex_feature_scores(selected_x, selected_y,
                                     mode=RuleInductionType.REGRESSION)[0]
      all_scores.append(scores)
  all_scores = pd.concat(all_scores, axis=1, sort=False)
  
  sns.boxplot(data=all_scores.T)
  plt.show()
  
subject: Update feature_scoring_flu_confidence.py