generate_min_max_population.py

def generate_min_max_population(df, constraints, generation_size):

  # The names, min-max and number of features are extracted from the DataFrame 
  features_nb = df.shape[1]
  features_names = df.columns
  df_min_max = df.describe().loc[["min","max"],:]

  # We initialize the new population DataFrame with zeros
  new_population = pd.DataFrame(np.zeros((generation_size,features_nb)), columns=features_names)
  
  # For each feature, we create a randomized array with a uniform distribution
  # between the observed min and max of the feature, except for the constrained
  # ones where the value is unique

  for column_name in features_names:
    if column_name in constraints.index:
      new_population[column_name] = np.ones(generation_size)*constraints["constrained_feature_value"].loc[column_name]
      
    else:  
      new_population[column_name] = np.random.uniform(df_min_max.loc["min",column_name], df_min_max.loc["max",column_name], generation_size)

  return new_population