Python Visualize Dictionary

def calculate_classification_metrics(test_targets, predictions) -> dict:
    """Calculation of accuracy score, F1 micro and F1 macro"""
    results = {
        'accuracy': accuracy_score(test_targets, predictions),
        'f1-micro': f1_score(test_targets, predictions, average="micro"),  # good for unbalanced classes
        'f1-macro': f1_score(test_targets, predictions, average="macro"),
        'precision': precision_score(test_targets, predictions, average="weighted"),
        'recall': recall_score(test_targets, predictions, average="weighted"),
    }
    # 'accuracy', 'f1-micro', 'f1-macro', 'precision', 'recall'
    print(f"\tAccuracy score: {results['accuracy']:.3f}")
    print(f"\tF1-micro: {results['f1-micro']:.3f}")
    print(f"\tF1-macro: {results['f1-macro']:.3f}")
    print(f"\tPrecision score: {results['precision']:.3f}")
    print(f"\tRecall score: {results['recall']:.3f}")

    return results

def feature_importances(model, feature_list: list, importance_limit: float = 0.05, display_top_n: int = 20,
                        display_graph=True):
    importances = list(model.feature_importances_)  # List of tuples with variable and importance

    # Sort the feature importances by most important first
    feature_importances = [(feature, round(importance, 2)) for feature, importance in
                           zip(feature_list, importances)]
    feature_importances = sorted(feature_importances, key=lambda x: x[1],
                                 reverse=True)  # Print out the feature and importances

    features_to_keep = []
    for i, pair in zip(range(display_top_n), feature_importances):
        feature, importance = pair
        if importance < importance_limit:
            break
        features_to_keep.append(feature)
        print(f'{i + 1:3}. Feature: {feature:20} Importance: {importance}')

    print(features_to_keep)

    if display_graph:
        features = list()
        importances = list()

        for feature, importance in feature_importances[:display_top_n]:
            features.append(feature)
            importances.append(importance)

        # convert to pd.DataFrame
        sns.set(style='darkgrid')
        plt.plot(features, importances)
        plt.xticks(rotation=90)
        plt.show()
    return features_to_keep


def visualize_dict(D, sort=True):
    if sort:
        D = {k: v for k, v in sorted(D.items(),
                                     key=lambda item: item[1])}

    df = pd.DataFrame(data=D, index=list(D.keys()))
    ax = sns.barplot(data=df)

    # adding the text labels
    rects = ax.patches
    labels = list(D.values())
    for rect, label in zip(rects, labels):
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width() / 2,
                height,
                label,
                ha='center',
                va='bottom')

    plt.show()