from sklearn.tree import DecisionTreeClassifier 

clf = DecisionTreeClassifier()

#hypertuning paramenters
    parameter_grid = {
    'criterion':['gini','entropy'],
    'splitter':['best','random'],
    'max_depth': [5, 8,10, 15, 25],
    'min_samples_split' : [2, 5, 10, 15,20,25],
    'min_samples_leaf' : [1, 2, 5, 10,15,20],
    'random_state' : [seed],
                    }
    
    print("TUNING ############################")
    startgrid=time.time()
    cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)
    grid_searchdt = FixedBayesSearchCV(estimator=clf, search_spaces=parameter_grid, n_jobs=-1, cv=cv,iid=False)
    bestDT = grid_searchdt.fit(x_train, y_train)
    print(bestDT.best_params_)
    best_grid_dt = bestDT.best_estimator_
    print(best_grid_dt)
    endgrid = time.time()
    print("Grid time: "+str(endgrid-startgrid))