trainers
========

.. py:module:: topmost.trainers




.. toctree::
   :titlesonly:
   :maxdepth: 3


   basic/index.rst


   crosslingual/index.rst


   dynamic/index.rst


   hierarchical/index.rst




Package Contents
----------------



.. autoapisummary::


   topmost.trainers.BasicTrainer


   topmost.trainers.BERTopicTrainer


   topmost.trainers.FASTopicTrainer


   topmost.trainers.LDAGensimTrainer


   topmost.trainers.LDASklearnTrainer


   topmost.trainers.NMFGensimTrainer


   topmost.trainers.NMFSklearnTrainer


   topmost.trainers.CrosslingualTrainer


   topmost.trainers.DynamicTrainer


   topmost.trainers.DTMTrainer


   topmost.trainers.HierarchicalTrainer


   topmost.trainers.HDPGensimTrainer





.. py:class:: BasicTrainer(model, dataset, num_top_words=15, epochs=200, learning_rate=0.002, batch_size=200, lr_scheduler=None, lr_step_size=125, log_interval=5, verbose=False)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: epochs
      :value: 200



   .. py:attribute:: learning_rate
      :value: 0.002



   .. py:attribute:: batch_size
      :value: 200



   .. py:attribute:: lr_scheduler
      :value: None



   .. py:attribute:: lr_step_size
      :value: 125



   .. py:attribute:: log_interval
      :value: 5



   .. py:attribute:: verbose
      :value: False



   .. py:method:: make_optimizer()


   .. py:method:: make_lr_scheduler(optimizer)


   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: BERTopicTrainer(dataset, num_topics=50, num_top_words=15)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:method:: train()


   .. py:method:: test(texts)


   .. py:method:: get_beta()


   .. py:method:: get_top_words()


   .. py:method:: export_theta()


.. py:class:: FASTopicTrainer(dataset, num_topics=50, num_top_words=15, preprocess=None, epochs=200, DT_alpha=3.0, TW_alpha=2.0, theta_temp=1.0, verbose=False)

   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: model


   .. py:attribute:: epochs
      :value: 200



   .. py:method:: train()


   .. py:method:: test(texts)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: LDAGensimTrainer(dataset, num_topics=50, num_top_words=15, max_iter=1, alpha='symmetric', eta=None, verbose=False)

   .. py:attribute:: dataset


   .. py:attribute:: num_topics
      :value: 50



   .. py:attribute:: vocab_size


   .. py:attribute:: max_iter
      :value: 1



   .. py:attribute:: alpha
      :value: 'symmetric'



   .. py:attribute:: eta
      :value: None



   .. py:attribute:: verbose
      :value: False



   .. py:attribute:: num_top_words
      :value: 15



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: LDASklearnTrainer(model, dataset, num_top_words=15, verbose=False)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: verbose
      :value: False



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: NMFGensimTrainer(dataset, num_topics=50, num_top_words=15, max_iter=1)

   .. py:attribute:: dataset


   .. py:attribute:: num_topics
      :value: 50



   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: vocab_size


   .. py:attribute:: max_iter
      :value: 1



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: NMFSklearnTrainer(model, dataset, num_top_words=15)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: CrosslingualTrainer(model, dataset, num_top_words=15, epochs=500, learning_rate=0.002, batch_size=200, lr_scheduler=None, lr_step_size=125, log_interval=5, verbose=False)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: epochs
      :value: 500



   .. py:attribute:: learning_rate
      :value: 0.002



   .. py:attribute:: batch_size
      :value: 200



   .. py:attribute:: lr_scheduler
      :value: None



   .. py:attribute:: lr_step_size
      :value: 125



   .. py:attribute:: log_interval
      :value: 5



   .. py:method:: make_optimizer()


   .. py:method:: make_lr_scheduler(optimizer)


   .. py:method:: train()


   .. py:method:: test(bow_en, bow_cn)


   .. py:method:: infer_theta(bow, lang)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: DynamicTrainer(model, dataset, num_top_words=15, epochs=200, learning_rate=0.002, batch_size=200, lr_scheduler=None, lr_step_size=125, log_interval=5, verbose=False)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: epochs
      :value: 200



   .. py:attribute:: learning_rate
      :value: 0.002



   .. py:attribute:: batch_size
      :value: 200



   .. py:attribute:: lr_scheduler
      :value: None



   .. py:attribute:: lr_step_size
      :value: 125



   .. py:attribute:: log_interval
      :value: 5



   .. py:attribute:: verbose
      :value: False



   .. py:method:: make_optimizer()


   .. py:method:: make_lr_scheduler(optimizer)


   .. py:method:: train()


   .. py:method:: test(bow, times)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: DTMTrainer(dataset, num_topics=50, num_top_words=15, alphas=0.01, chain_variance=0.005, passes=10, lda_inference_max_iter=25, em_min_iter=6, em_max_iter=20, verbose=False)

   .. py:attribute:: dataset


   .. py:attribute:: vocab_size


   .. py:attribute:: num_topics
      :value: 50



   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: alphas
      :value: 0.01



   .. py:attribute:: chain_variance
      :value: 0.005



   .. py:attribute:: passes
      :value: 10



   .. py:attribute:: lda_inference_max_iter
      :value: 25



   .. py:attribute:: em_min_iter
      :value: 6



   .. py:attribute:: em_max_iter
      :value: 20



   .. py:attribute:: verbose
      :value: False



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_theta()


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()


.. py:class:: HierarchicalTrainer(model, dataset, num_top_words=15, epochs=200, learning_rate=0.002, batch_size=200, lr_scheduler=None, lr_step_size=125, log_interval=5, verbose=False)

   .. py:attribute:: model


   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: epochs
      :value: 200



   .. py:attribute:: learning_rate
      :value: 0.002



   .. py:attribute:: batch_size
      :value: 200



   .. py:attribute:: lr_scheduler
      :value: None



   .. py:attribute:: lr_step_size
      :value: 125



   .. py:attribute:: log_interval
      :value: 5



   .. py:attribute:: verbose
      :value: False



   .. py:method:: make_optimizer()


   .. py:method:: make_lr_scheduler(optimizer)


   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_phi()


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None, annotation=False)


   .. py:method:: export_theta()


.. py:class:: HDPGensimTrainer(dataset, num_top_words=15, max_chunks=None, max_time=None, chunksize=256, kappa=1.0, tau=64.0, K=15, T=150, alpha=1, gamma=1, eta=0.01, scale=1.0, var_converge=0.0001, verbose=False)

   .. py:attribute:: dataset


   .. py:attribute:: num_top_words
      :value: 15



   .. py:attribute:: vocab_size


   .. py:attribute:: max_chunks
      :value: None



   .. py:attribute:: max_time
      :value: None



   .. py:attribute:: chunksize
      :value: 256



   .. py:attribute:: kappa
      :value: 1.0



   .. py:attribute:: tau
      :value: 64.0



   .. py:attribute:: K
      :value: 15



   .. py:attribute:: T
      :value: 150



   .. py:attribute:: alpha
      :value: 1



   .. py:attribute:: gamma
      :value: 1



   .. py:attribute:: eta
      :value: 0.01



   .. py:attribute:: scale
      :value: 1.0



   .. py:attribute:: var_converge
      :value: 0.0001



   .. py:attribute:: verbose
      :value: False



   .. py:method:: train()


   .. py:method:: test(bow)


   .. py:method:: get_beta()


   .. py:method:: get_top_words(num_top_words=None)


   .. py:method:: export_theta()