As described in our user guide for Latent Dirichlet Allocation (LDA), Hivemall enables you to apply clustering for your data based on a topic modeling technique. While LDA is one of the most popular techniques, there is another approach named Probabilistic Latent Semantic Analysis (pLSA). In fact, pLSA is the predecessor of LDA, but it has an advantage in terms of running time.

In order to efficiently handle large-scale data, our pLSA implementation is based on the following incremental variant of the original pLSA algorithm:


This feature is supported from Hivemall v0.5-rc.1 or later.


Basically, you can use our pLSA function in a similar way to LDA.

In particular, we have two pLSA functions, train_plsa() and plsa_predict(). These functions can be used almost interchangeably with train_lda() and lda_predict(). Thus, reading our user guide for LDA should be helpful before trying pLSA.

In short, for the sample docs table we introduced in the LDA tutorial:

docid doc
1 "Fruits and vegetables are healthy."
2 "I like apples, oranges, and avocados. I do not like the flu or colds."
... ...

a pLSA model can be built as follows:

with word_counts as (
    feature(word, count(word)) as f
    docs t1
    lateral view explode(tokenize(doc, true)) t2 as word
    not is_stopword(word)
  group by
    docid, word
input as (
  select docid, collect_list(f) as features
  from word_counts
  group by docid
  train_plsa(features, '-topics 2 -eps 0.00001 -iter 2048 -alpha 0.01') as (label, word, prob)
label word prob
0 like 0.28549945
0 colds 0.14294468
0 apples 0.14291435
0 avocados 0.1428958
0 flu 0.14287639
0 oranges 0.1428691
0 healthy 1.2605103E-7
0 fruits 4.772253E-8
0 vegetables 1.929087E-8
1 vegetables 0.32713377
1 fruits 0.32713372
1 healthy 0.3271335
1 like 0.006977764
1 oranges 0.0025642214
1 flu 0.002507711
1 avocados 0.0023572792
1 apples 0.002213457
1 colds 0.001978546

And prediction can be done as:

test as (
    count(word) as value
    docs t1
    LATERAL VIEW explode(tokenize(doc, true)) t2 as word
    not is_stopword(word)
  group by
    docid, word
topic as (
    plsa_predict(t.word, t.value, m.label, m.prob, '-topics 2') as probabilities
    test t
    JOIN plsa_model m ON (t.word = m.word)
  group by
  m.words -- topic each document should be assigned
  topic t 
  JOIN (
    select label, collect_list(feature(word, prob)) as words
    from plsa_model
    group by label
  ) m on t.probabilities[0].label = m.label
docid probabilities label m.words
1 [{"label":1,"probability":0.72298235},{"label":0,"probability":0.27701768}] 1 ["vegetables:0.32713377","fruits:0.32713372","healthy:0.3271335","like:0.006977764","oranges:0.0025642214","flu:0.002507711","avocados:0.0023572792","apples:0.002213457","colds:0.001978546"]
2 [{"label":0,"probability":0.7052526},{"label":1,"probability":0.2947474}] 0 ["like:0.28549945","colds:0.14294468","apples:0.14291435","avocados:0.1428958","flu:0.14287639","oranges:0.1428691","healthy:1.2605103E-7","fruits:4.772253E-8","vegetables:1.929087E-8"]

Difference with LDA

The main advantage of using pLSA is its efficiency. Since mathematical formulation and optimization logic is much simpler than LDA, using pLSA generally requires much shorter running time.

In terms of accuracy, LDA could be better than pLSA. For example, a word like appears twice in the above sample document#2 gets larger probabilities both in topic#1 and #2, even though one document does not contain the word. By contrast, LDA results (i.e., lambda values) are more clearly separated as shown in the LDA page. Thus, a pLSA model is likely to be biased.

For the reasons that we mentioned above, we recommend you to first use LDA. After that, if you encountered problems such as slow running time and undesirable clustering results, let you try alternative pLSA approach.

Setting hyper-parameter alpha

For training pLSA, we set a hyper-parameter alpha in the above example:

SELECT train_plsa(feature, '-topics 2 -eps 0.00001 -iter 2048 -alpha 0.01')

This value controls how much iterative model update is affected by the old results.

From an algorithmic point of view, training pLSA (and LDA) iteratively repeats certain operations and updates the target value (i.e., probability obtained as a result of train_plsa()). This iterative procedure gradually makes the probabilities more accurate. What alpha does is to control the degree of the change of probabilities in each step.

Importantly, pLSA is likely to overfit single mini-batch. As a result, P(wz)P(w|z) could be particularly bad values (i.e., (wz)=0(w|z) = 0), and train_plsa() sometimes fails with an exception like:

Perplexity would be Infinity. Try different mini-batch size `-s`, larger `-delta` and/or larger `-alpha`.

In that case, you need to try different hyper-parameters to avoid overfitting as the exception suggests.

For instance, 20 newsgroups dataset which consists of 10906 realistic documents empirically requires the following options:

SELECT train_plsa(features, '-topics 20 -iter 10 -s 128 -delta 0.01 -alpha 512 -eps 0.1')

Clearly, alpha is much larger than 0.01 which was used for the dummy data above. Let you keep in mind that an appropriate value of alpha highly depends on the number of documents and mini-batch size.

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