human
Can you provide a the Python program you requested given the following:
I am working on the file "Luigi_airflow_006.ipynb".
The first task is:
Tuning
Can you write Python program for it?
assistant
#Here is the Python program you requested.

from sklearn import ensemble

warnings.filterwarnings("ignore")

# Parameters for Random Forest

parameters = [
  {'max_depth': [8,10,12,14,16,18], 
   'min_samples_split': [10,15,20,25,30], 
   'min_samples_leaf': [10,15,20,25,30]
  }
]
scores = ['precision', 'recall']

RFModel = ensemble.RandomForestClassifier(n_estimators=5, oob_score=False)

class TuneParameters():
    def __init__(self, explanatory_vars, explained_var, estimator, parameters, scores):
        self.explanatory_vars = explanatory_vars
        self.explained_var = explained_var
        self.estimator = estimator
        self.parameters = parameters
        self.scores = scores
    
    def create_train_test_splits(self):
        X_train, X_test, y_train, y_test = train_test_split(self.explanatory_vars, self.explained_var, 
                                                            test_size = 0.5, random_state = 0,
                                                            stratify = self.explained_var)
        return X_train, X_test, y_train, y_test 
    
    def tuning_parameters(self, trainset_x, testset_x, trainset_y, testset_y):
        for score in self.scores:
            print("# Tuning hyper-parameters for %s" % score)
            print("")

            grid_rfc = GridSearchCV(self.estimator, self.parameters,n_jobs=100, cv=10, refit=True,
                               scoring='%s_macro' % score)
            grid_rfc.fit(trainset_x, trainset_y)

            print("Best parameters set found on development set:")
            print("")
            print(grid_rfc.best_params_)
            print("")
            y_true, y_pred = testset_y, grid_rfc.predict(testset_x)
            print(classification_report(y_true, y_pred))
            print("")

            best_parameters = grid_rfc.best_estimator_.get_params()
            return best_parameters
        
    def combiner_pipeline(self):
        X_train, X_test, y_train, y_test = self.create_train_test_splits()
        best_params = self.tuning_parameters(X_train, X_test, y_train,...