ADD: usages of is_convergent and inference_multi_processors

Author: JoeZJH

README.md

@@ -47,30 +47,45 @@ import model.labeled_lda as llda
 # data
 labeled_documents = [("example example example example example", ["example"]),
  ("test llda model test llda model test llda model", ["test", "llda_model"]),
- ("example test example test example test example test", ["example", "test"])]
+ ("example test example test example test example test", ["example", "test"]),
+ ("good perfect good good perfect good good perfect good ", ["positive"]),
+ ("bad bad down down bad bad down", ["negative"])]
 
 # new a Labeled LDA model
+# llda_model = llda.LldaModel(labeled_documents=labeled_documents, alpha_vector="50_div_K", eta_vector=0.001)
+# llda_model = llda.LldaModel(labeled_documents=labeled_documents, alpha_vector=0.02, eta_vector=0.002)
 llda_model = llda.LldaModel(labeled_documents=labeled_documents)
 print llda_model
 
 # training
-llda_model.training(iteration=10, log=True)
+# llda_model.training(iteration=10, log=True)
+while True:
+ print("iteration %s sampling..." % (llda_model.iteration + 1))
+ llda_model.training(1)
+ print "after iteration: %s, perplexity: %s" % (llda_model.iteration, llda_model.perplexity)
+ if llda_model.is_convergent:
+ break
 
 # update
 print "before updating: ", llda_model
-update_labeled_documents = [("good perfect good good perfect good good perfect good ", ["positive"]),
- ("bad bad down down bad", ["negative"]),
- ("new example test example test example test example test", ["example", "test"])]
+update_labeled_documents = [("new example test example test example test example test", ["example", "test"])]
 llda_model.update(labeled_documents=update_labeled_documents)
 print "after updating: ", llda_model
 
 # train again
-llda_model.training(iteration=10, log=True)
+# llda_model.training(iteration=10, log=True)
+while True:
+ print("iteration %s sampling..." % (llda_model.iteration + 1))
+ llda_model.training(1)
+ print "after iteration: %s, perplexity: %s" % (llda_model.iteration, llda_model.perplexity)
+ if llda_model.is_convergent:
+ break
 
 # inference
 # note: the result topics may be different for difference training, because gibbs sampling is a random algorithm
-document = "test example llda model test example test example good perfect good perfect good perfect"
-topics = llda_model.inference(document=document, iteration=10, times=10)
+document = "example llda model example example good perfect good perfect good perfect"
+# topics = llda_model.inference(document=document, iteration=10, times=10)
+topics = llda_model.inference_multi_processors(document=document, iteration=10, times=10)
 print topics
 
 # save to disk

example/example.py

@@ -14,7 +14,13 @@
 print llda_model
 
 # training
-llda_model.training(iteration=10, log=True)
+# llda_model.training(iteration=10, log=True)
+while True:
+ print("iteration %s sampling..." % (llda_model.iteration + 1))
+ llda_model.training(1)
+ print "after iteration: %s, perplexity: %s" % (llda_model.iteration, llda_model.perplexity)
+ if llda_model.is_convergent:
+ break
 
 # update
 print "before updating: ", llda_model
@@ -23,12 +29,19 @@
 print "after updating: ", llda_model
 
 # train again
-llda_model.training(iteration=10, log=True)
+# llda_model.training(iteration=10, log=True)
+while True:
+ print("iteration %s sampling..." % (llda_model.iteration + 1))
+ llda_model.training(1)
+ print "after iteration: %s, perplexity: %s" % (llda_model.iteration, llda_model.perplexity)
+ if llda_model.is_convergent:
+ break
 
 # inference
 # note: the result topics may be different for difference training, because gibbs sampling is a random algorithm
 document = "example llda model example example good perfect good perfect good perfect"
-topics = llda_model.inference(document=document, iteration=10, times=10)
+# topics = llda_model.inference(document=document, iteration=10, times=10)
+topics = llda_model.inference_multi_processors(document=document, iteration=10, times=10)
 print topics
 
 # save to disk