old_contents: import pandas as pd
  from sklearn import ensemble
  
  from love_matcher.refactored.feature_engineering.feature_engineering import FeatureEngineering
  from love_matcher.refactored.preprocessing.raw_set_processing import RawSetProcessing
  from love_matcher.refactored.training.training import Trainer
  from love_matcher.refactored.tuning.tuning import TuneParameters
  from docs.conf import *
  
  
  class MainClass:
      # TODO make workspace configurable from luigi.cfg
      def __init__(self, workspace="/home/dolounet/dev/workshops/"):
          self.workspace = workspace
  
      def main(self):
          raw_dataset = self.read_dataframe()
  
          # Preprocessing
          raw_dataset = RawSetProcessing(my_variables_selection, dataframe=raw_dataset)
          dataset_df = raw_dataset.combiner_pipeline()
  
          # Feature engineering
          feature_engineering = FeatureEngineering()
          feat_eng_df = feature_engineering.get_partner_features(dataset_df)
  
          # TODO make random forest parameters externalized
          rf_model = ensemble.RandomForestClassifier(n_estimators=5, oob_score=False)
          tune = TuneParameters(feat_eng_df, rf_model, parameters, scores, features)
          best_parameters = tune.combiner_pipeline()
          x_train, x_test, y_train, y_test = tune.create_train_test_splits()
  
          # Train
          train = Trainer(x_train, y_train, x_test, y_test, best_parameters)
          estimator, score_train, score_test = train.combiner_pipeline()
  
      def read_dataframe(self):
          return pd.read_csv(self.workspace + "Speed_Dating_Data.csv", encoding="ISO-8859-1")
  
      def process_features_names(self, features, suffix_1, suffix_2):
          features_me = [feat + suffix_1 for feat in features]
          features_partner = [feat + suffix_2 for feat in features]
          features_all = features_me + features_partner
          return features_all
  
  
  # add suffix to each element of list
  
  
  
new_contents: import pandas as pd
  from sklearn import ensemble
  
  from love_matcher.refactored.feature_engineering.feature_engineering import FeatureEngineering
  from love_matcher.refactored.preprocessing.raw_set_processing import RawSetProcessing
  from love_matcher.refactored.training.training import Trainer
  from love_matcher.refactored.tuning.tuning import TuneParameters
  from docs.conf import *
  
  
  class MainClass:
      # TODO make workspace configurable from luigi.cfg
      def __init__(self, workspace="/home/dolounet/dev/workshops/"):
          self.workspace = workspace
  
      def main(self):
          raw_dataset = self.read_dataframe()
  
          # Preprocessing
          raw_dataset = RawSetProcessing(my_variables_selection, dataframe=raw_dataset)
          dataset_df = raw_dataset.combiner_pipeline()
  
          # Feature engineering
          feature_engineering = FeatureEngineering()
          feat_eng_df = feature_engineering.get_partner_features(dataset_df)
  
          # TODO make random forest parameters externalized
          rf_model = ensemble.RandomForestClassifier(n_estimators=5, oob_score=False)
          tune = TuneParameters(feat_eng_df, rf_model, parameters, scores, features)
          best_parameters = tune.combiner_pipeline()
          x_train, x_test, y_train, y_test = tune.create_train_test_splits()
  
          # Train
          train = Trainer(x_train, y_train, x_test, y_test, best_parameters)
          estimator, score_train, score_test = train.combiner_pipeline()
  
      def read_dataframe(self):
          return pd.read_csv(self.workspace + "Speed_Dating_Data.csv", encoding="ISO-8859-1")
  
  
  
subject: remove process suffix func from main