gensim.models.CoherenceModel
¶
-
class
gensim.models.
CoherenceModel
(model=None, topics=None, texts=None, corpus=None, dictionary=None, window_size=None, coherence='c_v', topn=10)[source]¶ Objects of this class allow for building and maintaining a model for topic coherence.
The main methods are:
- constructor, which initializes the four stage pipeline by accepting a coherence measure,
- the
get_coherence()
method, which returns the topic coherence.
One way of using this feature is through providing a trained topic model. A dictionary has to be explicitly provided if the model does not contain a dictionary already. >>> cm = CoherenceModel(model=tm, corpus=corpus, coherence=’u_mass’) # tm is the trained topic model >>> cm.get_coherence()
Another way of using this feature is through providing tokenized topics such as: >>> topics = [[‘human’, ‘computer’, ‘system’, ‘interface’],
[‘graph’, ‘minors’, ‘trees’, ‘eps’]]>>> cm = CoherenceModel(topics=topics, corpus=corpus, dictionary=dictionary, coherence='u_mass') # note that a dictionary has to be provided. >>> cm.get_coherence()
Model persistency is achieved via its load/save methods.
Methods¶
__init__ ([model, topics, texts, corpus, ...]) |
Args: —- model : Pre-trained topic model. |
get_coherence () |
Return coherence value based on pipeline parameters. |
load (fname[, mmap]) |
Load a previously saved object from file (also see save). |
save (fname_or_handle[, separately, ...]) |
Save the object to file (also see load). |