plot_eli5_top_explanations.py

def plot_eli5_top_explanations(
    model: Model, 
    image: np.array, 
    class_names_mapping: Dict[int, str],
    top_preds_count: int = 3,
    fig_name: Optional[str] = None
) -> None:
    
    image_columns = 3
    image_rows = math.ceil(top_preds_count / image_columns)
    
    preds = model.predict(image)
    top_preds_indexes = np.flip(np.argsort(preds))[0,:top_preds_count]
    top_preds_values = preds.take(top_preds_indexes)
    top_preds_names = np.vectorize(lambda x: class_names[x])(top_preds_indexes)
    
    plt.style.use('dark_background')
    fig, axes = plt.subplots(image_rows, image_columns, figsize=(image_columns * 5, image_rows * 5))
    [ax.set_axis_off() for ax in axes.flat]
    
    for i, (index, value, name, ax) in \
        enumerate(zip(top_preds_indexes, top_preds_values, top_preds_names, axes.flat)):
            
        class_grad_cam = eli5.show_prediction(model, image, targets=[int(index)])
        subplot_title = "{}. class: {} pred: {:.3f}".format(i + 1, name, value)
        ax.imshow(class_grad_cam)
        ax.set_title(subplot_title, pad=20)
    
    if fig_name:
        plt.savefig(fig_name)
    plt.show()