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,
)