joezjh / labeled-lda-python Goto Github PK

Halo Joe, Thank you again for the coding and your last answer to my question. If you are glad, I want to ask some questions about this code.

1. What value do you use to evaluate the model? Is that perplexity? What is the meaning of the perplexity in LLDA?
2. What is the difference between llda.LldaModel, .training, and .inference? Which one did you use to build a model?
3. How much delta should be input?

after iteration: 51, perplexity: 5931.100267564532
delta beta: 5.883340980101
before updating:  
Labeled-LDA Model:
	K = 61
	M = 60120
	T = 130755
	WN = 4029682
	LN = 60120
	alpha = 0.01
	eta = 0.001
	perplexity = 5931.100267564532
	
after updating:  
Labeled-LDA Model:
	K = 61
	M = 60121
	T = 130763
	WN = 4029695
	LN = 60123
	alpha = 0.01
	eta = 0.001
	perplexity = 5931.23254749438
	
iteration 52 sampling...
Traceback (most recent call last):
  File "/Users/rr/opt/anaconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/Users/rr/opt/anaconda3/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/Users/rr/labeled_lda/mt_llda.py", line 48, in train_model
    llda_model.training(1)
  File "/Users/rr/labeled_lda/labeled_lda.py", line 440, in training
    self._gibbs_sample_training()
  File "/Users/rr/labeled_lda/labeled_lda.py", line 272, in _gibbs_sample_training
    sample_z = LldaModel._multinomial_sample(p_vector)
  File "/Users/rr/labeled_lda/labeled_lda.py", line 218, in _multinomial_sample
    return np.random.multinomial(1, p_vector).argmax()
  File "mtrand.pyx", line 3863, in numpy.random.mtrand.RandomState.multinomial
  File "common.pyx", line 323, in numpy.random.common.check_array_constraint
ValueError: pvals < 0, pvals > 1 or pvals contains NaNs

where the line 47 and line 48

File "/Users/rr/labeled_lda/mt_llda.py", line 48, in train_model
    llda_model.training(1)

creates the object of labeled lda and sends it to training:

llda_model = llda.LldaModel(labeled_documents=labeled_documents, alpha_vector=0.01)
llda_model.training(1)

Dear,

First of all, thank you very much for your sharing a nice program.

I have a question related to the input labeled documents as follow
Each document has a following number, for example, labeled_documents = [("text" * 10, ['label_1]), ...],. In this case, what is 10 used for? How does it affect the training process? For example, if we change these values or even ignore them.

Thank you very much in advance!
Best regards,
Son.

Also I want to ask how did you know the order of the topic and term from this topic term matrix? (which one is the label 1,2,3 and words order) Thankyouuu

Hai Joe, thankyou so much for the coding. I want to ask about common topic. What is the interpretation for that? I really appreciate your answer because right now I'm doing my undergraduate thesis using this code

Thank you very much for sharing your code.
When I download your example.py and run it, I found an error occured when I use
'''llda_model_new.load_model_from_dir(save_model_dir, load_derivative_properties=False)'''.

And here is the traceback:
'''
Traceback (most recent call last):

File "", line 1, in
runfile('D:/putong/taxiLDA/Labeled-LDA-Python-master/example/example.py', wdir='D:/putong/taxiLDA/Labeled-LDA-Python-master/example')

File "D:\ana\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)

File "D:\ana\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)

File "D:/putong/taxiLDA/Labeled-LDA-Python-master/example/example.py", line 78, in
llda_model_new.load_model_from_dir(save_model_dir, load_derivative_properties=False)

File "..\model\labeled_lda.py", line 794, in load_model_from_dir
self._initialize_derivative_fields()

File "..\model\labeled_lda.py", line 126, in _initialize_derivative_fields
self.alpha_vector_Lambda = self.alpha_vector * self.Lambda

ValueError: operands could not be broadcast together with shapes (0,) (6,6)
'''
I think something goes wrong when the program loads and reads the model.