ADD: add example for README.md

Author: JoeZJH

README.md

@@ -42,7 +42,77 @@ Labeled LDA can directly learn topics(tags) correspondences.
 ```
 # @source code: example/exapmle.py
 
+import model.labeled_lda as llda
 
+# initialize data
+labeled_documents = [("example example example example example"*10, ["example"]),
+ ("test llda model test llda model test llda model"*10, ["test", "llda_model"]),
+ ("example test example test example test example test"*10, ["example", "test"]),
+ ("good perfect good good perfect good good perfect good "*10, ["positive"]),
+ ("bad bad down down bad bad down"*10, ["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, alpha_vector=0.01)
+print llda_model
+
+# training
+# 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())
+ print "delta beta: %s" % llda_model.delta_beta
+ if llda_model.is_convergent(method="beta", delta=0.01):
+ break
+
+# update
+print "before updating: ", llda_model
+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)
+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())
+ print "delta beta: %s" % llda_model.delta_beta
+ if llda_model.is_convergent(method="beta", delta=0.01):
+ 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" * 100
+
+topics = llda_model.inference(document=document, iteration=100, times=10)
+print topics
+
+# perplexity
+# calculate perplexity on test data
+perplexity = llda_model.perplexity(documents=["example example example example example",
+ "test llda model test llda model test llda model",
+ "example test example test example test example test",
+ "good perfect good good perfect good good perfect good",
+ "bad bad down down bad bad down"],
+ iteration=30,
+ times=10)
+print "perplexity on test data: %s" % perplexity
+# calculate perplexity on training data
+print "perplexity on training data: %s" % llda_model.perplexity()
+
+# save to disk
+save_model_dir = "../data/model"
+# llda_model.save_model_to_dir(save_model_dir, save_derivative_properties=True)
+llda_model.save_model_to_dir(save_model_dir)
+
+# load from disk
+llda_model_new = llda.LldaModel()
+llda_model_new.load_model_from_dir(save_model_dir, load_derivative_properties=False)
+print "llda_model_new", llda_model_new
+print "llda_model", llda_model
 ```