approximate nearest neighbors with annoy.py

# for images similar to centroid 
def get_similar_images_annoy_centroid(annoy_tree, vector_value, number_of_items=12):
    start = time.time()
    similar_img_ids = annoy_tree.get_nns_by_vector(vector_value, number_of_items+1)
    end = time.time()
    print(f'{(end - start) * 1000} ms')
    # ignore first item as it is always target image
    return data_df_ouput.iloc[similar_img_ids[1:]] 


def show_similar_images(similar_images_df, fig_size=[10,10], hide_labels=True):
    if hide_labels:
        category_list = []
        for i in range(len(similar_images_df)):
            # replace category with blank so it wont show in display
            category_list.append(CategoryList(similar_images_df['label_id'].values*0,
                                              [''] * len(similar_images_df)).get(i))
    else:
        category_list = [learner.data.train_ds.y.reconstruct(y) for y in similar_images_df['label_id']]
    return learner.data.show_xys([open_image(img_id) for img_id in similar_images_df['img_path']],
                                category_list, figsize=fig_size)