def top_topics(self, len_topic: int, top_n: int = 10, return_df: bool = True): """ Print important topics based on decomposition. Parameters ---------- len_topic: int """ return print_topics_modelling( len_topic, feature_names=np.array(self._features), sorting=np.argsort(self._components)[:, ::-1], n_words=top_n, return_df=return_df, )
def top_topics(self, len_topic: int, top_n: int = 10, return_df: bool = True): """ Print important topics based on decomposition. Parameters ---------- len_topic: int size of topics. top_n: int, optional (default=10) top n of each topic. return_df: bool, optional (default=True) return as pandas.DataFrame, else JSON. """ return print_topics_modelling( len_topic, feature_names=np.array(self._features), sorting=np.argsort(self._components)[:, ::-1], n_words=top_n, return_df=return_df, )