def convertldaMalletToldaGen(mallet_model):
model_gensim = LdaModel(id2word=mallet_model.id2word,
num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha)
model_gensim.state.sstats[...] = mallet_model.wordtopics
model_gensim.sync_state()
return model_gensim
def malletmodel2ldamodel(mallet_model, gamma_threshold=0.001, iterations=50):
"""Convert :class:`~gensim.models.wrappers.ldamallet.LdaMallet` to :class:`~gensim.models.ldamodel.LdaModel`.
This works by copying the training model weights (alpha, beta...) from a trained mallet model into the gensim model.
Parameters
----------
mallet_model : :class:`~gensim.models.wrappers.ldamallet.LdaMallet`
Trained Mallet model
gamma_threshold : float, optional
To be used for inference in the new LdaModel.
iterations : int, optional
Number of iterations to be used for inference in the new LdaModel.
Returns
-------
:class:`~gensim.models.ldamodel.LdaModel`
Gensim native LDA.
"""
model_gensim = LdaModel(
id2word=mallet_model.id2word,
num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha,
eta=0,
iterations=iterations,
gamma_threshold=gamma_threshold,
dtype=numpy.
float64 # don't loose precision when converting from MALLET
)
model_gensim.state.sstats[...] = mallet_model.wordtopics
model_gensim.sync_state()
return model_gensim
def ldaMalletConvertToldaGen(mallet_model):
model_gensim = LdaModel(id2word=mallet_model.id2word,
num_topics=mallet_model.num_topics,
alpha=mallet_model.alpha,
eta=0,
iterations=1000,
gamma_threshold=0.001,
dtype=np.float32)
model_gensim.state.sstats[...] = mallet_model.wordtopics
model_gensim.sync_state()
return model_gensim