Fix errors in eva function declaration

from sklearn.metrics import RocCurveDisplay

from sklearn.preprocessing import OrdinalEncoder

import seaborn as sns

from parameter_opt import parameter_dectree_opt, parameter_rndfor_opt #, parameter_naibay_opt

from validation_curve import check_fitting_rndfor, check_fitting_dectree

from parameter_opt import parameter_dectree_opt, parameter_rndfor_opt, check_fitting_rndfor, check_fitting_dectree #, parameter_naibay_opt

from feature_importance import feature_importance_NB, feature_importance_rndfor, feature_importance_dectree

from tree_plotting import plot_dec_boundary, plot_tree, plot_SVM

from tree_plotting import plot_dectree_boundary, plot_tree, plot_SVM

from sklearn.svm import SVC

from sklearn.neural_network import MLPClassifier

from evaluation_functions import calculate_statistical_characteristics, plot_roc_curves_avarage, \

classifiers = [

    dec_tree,

    rnd_forr,

    #naive_bayes,

    naive_bayes,

    #naive_bayes_ws,

    svc,

    mlp

classifiers_names = [

    "Decision Tree",

    "Random Forest",

    #"Naive Bayes without smoothing",

    "Naive Bayes",

    #"Naive Bayes with smoothing",

    "Support Vector Machines",

    "Multilayer Perceptron"

    # parameter_naibay_opt(X_train, y_train) 

if __name__ == "__main__":

    # create dict for classifier models

    ft_cl_models = {}

            if "Naive Bayes" in name_classifier:

                ordinal_enc = OrdinalEncoder(handle_unknown='use_encoded_value')

                ordinal_enc.fit(y_global)

                ordinal_enc = OrdinalEncoder(handle_unknown='use_encoded_value',

                                             unknown_value=-1)

                ordinal_enc.fit(X_global)

            classifier.fit(X_train, y_train)

            cl_models[name_classifier] = classifier

            # if feature_variations_name=="features_most_important":

            #     if name_classifier == "SVC":

            #         plot_SVM(svc, X_train, y_train, "SVC")

            #     elif classifier != naive_bayes and classifier != naive_bayes_ws:

            #         plot_dec_boundary(X_train, y_train, classifier, name_classifier)

            #     elif classifier != naive_bayes and classifier != naive_bayes:

            #         plot_dectree_boundary(X_train, y_train, classifier, name_classifier)

            # if features == features_all and classifier == dec_tree:

            #     feature_importance_dectree(classifier, features)

            #     check_fitting_dectree(X_train, y_train, dec_tree)

            # elif features == features_all and classifier == rnd_forr:

            #     feature_importance_rndfor(classifier, features, X_dev, y_dev)

            #     check_fitting_rndfor(X_train, y_train, rnd_forr)

            # elif features == features_all and classifier == naive_bayes:

            #     feature_importance_NB(classifier, X_dev, y_dev, features)

# create dataframe with all accuracies

all_accuracies, all_accuracies_sorted = collect_accuracies_in_data_frame(feature_variations_names, ft_cl_eva, print_results = print_results) 

all_accuracies, all_accuracies_sorted = collect_accuracies_in_data_frame(feature_variations_names, classifiers_names, ft_cl_eva, print_results = print_results) 

# plot accuracies

plot_accuracies(classifiers_names, feature_variations_names, sets)

plot_accuracies(classifiers_names, feature_variations_names, sets,  ft_cl_eva)

# visualize confusion matrix of best model

visualize_confusion_matrix_of_best_model(all_accuracies_sorted)

visualize_confusion_matrix_of_best_model(all_accuracies_sorted, ft_cl_confusion_matrices)

# # plot ROC curve for best model #TODO

# plot_roc_curves_svm(ft_cl_models[best_ft][best_cl], X_train, y_train, X_test, y_test, save_as='multiple_roc_curve.png')

 No newline at end of file

    for name in classifiers_names:

        labels.append(as_title(name)) 

    for set in sets:

    all_fig, all_ax = plt.subplots(1, len(sets))

    for set, index in zip(sets, range(len(sets))):

        ft_acc = []

        for ft in feature_variations_names:

            ft_acc.append(nested_evaluation_data[ft][set].loc['Accuracy'].tolist())

        x = np.arange(len(labels))

        # plot accuracies 

        fig, ax = plt.subplots()

        fig, ax = all_ax[0, index].subplots()

        ft_acc_subplots = []

        position = x - width/2

        for ft, ft_names in zip(ft_acc, feature_variations_names):

        ax.set_xticklabels(labels, rotation=90)

        ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))

        # save and display plots

        fig.tight_layout()

    # save and display plots

    all_fig.tight_layout()

    plt.ylim(ymax = 1, ymin = 0)

    if save:

        plt.savefig(set + "_accuracies_barplot.png", bbox_inches='tight')