def load_imdb_df(dirpath=os.path.join(BIGDATA_PATH, 'aclImdb'), subdirectories=(('train', 'test'), ('pos', 'neg', 'unsup'))):
    """ Walk directory tree starting at `path` to compile a DataFrame of movie review text labeled with their 1-10 star ratings

    Returns:
      DataFrame: columns=['url', 'rating', 'text'], index=MultiIndex(['train_test', 'pos_neg_unsup', 'id'])

    TODO:
      Make this more robust/general by allowing the subdirectories to be None and find all the subdirs containing txt files

    >> imdb_df().head()
                                                          url  rating                                               text
    index0 index1 index2
    train  pos    0       http://www.imdb.com/title/tt0453418       9  Bromwell High is a cartoon comedy. It ran at t...
                  1       http://www.imdb.com/title/tt0210075       7  If you like adult comedy cartoons, like South ...
                  2       http://www.imdb.com/title/tt0085688       9  Bromwell High is nothing short of brilliant. E...
                  3       http://www.imdb.com/title/tt0033022      10  "All the world's a stage and its people actors...
                  4       http://www.imdb.com/title/tt0043137       8  FUTZ is the only show preserved from the exper...
    """
    dfs = {}
    for subdirs in tqdm(list(product(*subdirectories))):
        urlspath = os.path.join(dirpath, subdirs[0], 'urls_{}.txt'.format(subdirs[1]))
        if not os.path.isfile(urlspath):
            if subdirs != ('test', 'unsup'):  # test/ dir doesn't usually have an unsup subdirectory
                logger.warning('Unable to find expected IMDB review list of URLs: {}'.format(urlspath))
            continue
        df = pd.read_csv(urlspath, header=None, names=['url'])
        # df.index.name = 'id'
        df['url'] = series_strip(df.url, endswith='/usercomments')

        textsdir = os.path.join(dirpath, subdirs[0], subdirs[1])
        if not os.path.isdir(textsdir):
            logger.warning('Unable to find expected IMDB review text subdirectory: {}'.format(textsdir))
            continue
        filenames = [fn for fn in os.listdir(textsdir) if fn.lower().endswith('.txt')]
        df['index0'] = subdirs[0]  # TODO: column names more generic so will work on other datasets
        df['index1'] = subdirs[1]
        df['index2'] = np.array([int(fn[:-4].split('_')[0]) for fn in filenames])
        df['rating'] = np.array([int(fn[:-4].split('_')[1]) for fn in filenames])
        texts = []
        for fn in filenames:
            with ensure_open(os.path.join(textsdir, fn)) as f:
                texts.append(f.read())
        df['text'] = np.array(texts)
        del texts
        df.set_index('index0 index1 index2'.split(), inplace=True)
        df.sort_index(inplace=True)
        dfs[subdirs] = df
    return pd.concat(dfs.values())
Walk directory tree starting at `path` to compile a DataFrame of movie review text labeled with their 1-10 star ratings

    Returns:
      DataFrame: columns=['url', 'rating', 'text'], index=MultiIndex(['train_test', 'pos_neg_unsup', 'id'])

    TODO:
      Make this more robust/general by allowing the subdirectories to be None and find all the subdirs containing txt files

    >> imdb_df().head()
                                                          url  rating                                               text
    index0 index1 index2
    train  pos    0       http://www.imdb.com/title/tt0453418       9  Bromwell High is a cartoon comedy. It ran at t...
                  1       http://www.imdb.com/title/tt0210075       7  If you like adult comedy cartoons, like South ...
                  2       http://www.imdb.com/title/tt0085688       9  Bromwell High is nothing short of brilliant. E...
                  3       http://www.imdb.com/title/tt0033022      10  "All the world's a stage and its people actors...
                  4       http://www.imdb.com/title/tt0043137       8  FUTZ is the only show preserved from the exper...