neurosynth/analysis/reduce.py
def _get_top_words(model, feature_names, n_top_words=40):
    """ Return top forty words from each topic in trained topic model.
    """
    topic_words = []
    for topic in model.components_:
        top_words = [feature_names[i] for i in topic.argsort()[:-n_top_words-1:-1]]
        topic_words += [top_words]
    return topic_words
def _get_top_words(model, feature_names, n_top_words=40):
    topic_words = []
    for topic in model.components_:
        top_words = [feature_names[i] for i in topic.argsort()[:-n_top_words-1:-1]]
        topic_words += [top_words]
    return topic_words
def _get_top_words ( model , feature_names , n_top_words = 40 ) : topic_words = [ ] for topic in model . components_ : top_words = [ feature_names [ i ] for i in topic . argsort ( ) [ : - n_top_words - 1 : - 1 ] ] topic_words += [ top_words ] return topic_words
Return top forty words from each topic in trained topic model.
Return top forty words from each topic in trained topic model .