Demonstration of the topic coherence pipeline in Gensim

Introduction

We will be using the u_mass and c_v coherence for two different LDA models: a "good" and a "bad" LDA model. The good LDA model will be trained over 50 iterations and the bad one for 1 iteration. Hence in theory, the good LDA model will be able come up with better or more human-understandable topics. Therefore the coherence measure output for the good LDA model should be more (better) than that for the bad LDA model. This is because, simply, the good LDA model usually comes up with better topics that are more human interpretable.



In [1]:

    
from __future__ import print_function

import os
import logging
import json
import warnings

try:
    raise ImportError
    import pyLDAvis.gensim
    CAN_VISUALIZE = True
    pyLDAvis.enable_notebook()
    from IPython.display import display
except ImportError:
    ValueError("SKIP: please install pyLDAvis")
    CAN_VISUALIZE = False

import numpy as np

from gensim.models import CoherenceModel, LdaModel, HdpModel
from gensim.models.wrappers import LdaVowpalWabbit, LdaMallet
from gensim.corpora import Dictionary

warnings.filterwarnings('ignore')  # To ignore all warnings that arise here to enhance clarity

Set up corpus

As stated in table 2 from this paper, this corpus essentially has two classes of documents. First five are about human-computer interaction and the other four are about graphs. We will be setting up two LDA models. One with 50 iterations of training and the other with just 1. Hence the one with 50 iterations ("better" model) should be able to capture this underlying pattern of the corpus better than the "bad" LDA model. Therefore, in theory, our topic coherence for the good LDA model should be greater than the one for the bad LDA model.



In [2]:

    
texts = [['human', 'interface', 'computer'],
         ['survey', 'user', 'computer', 'system', 'response', 'time'],
         ['eps', 'user', 'interface', 'system'],
         ['system', 'human', 'system', 'eps'],
         ['user', 'response', 'time'],
         ['trees'],
         ['graph', 'trees'],
         ['graph', 'minors', 'trees'],
         ['graph', 'minors', 'survey']]



In [3]:

    
dictionary = Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]

Set up two topic models

We'll be setting up two different LDA Topic models. A good one and bad one. To build a "good" topic model, we'll simply train it using more iterations than the bad one. Therefore the u_mass coherence should in theory be better for the good model than the bad one since it would be producing more "human-interpretable" topics.



In [4]:

    
goodLdaModel = LdaModel(corpus=corpus, id2word=dictionary, iterations=50, num_topics=2)
badLdaModel = LdaModel(corpus=corpus, id2word=dictionary, iterations=1, num_topics=2)

Using U_Mass Coherence



In [5]:

    
goodcm = CoherenceModel(model=goodLdaModel, corpus=corpus, dictionary=dictionary, coherence='u_mass')
badcm = CoherenceModel(model=badLdaModel, corpus=corpus, dictionary=dictionary, coherence='u_mass')

View the pipeline parameters for one coherence model

Following are the pipeline parameters for u_mass coherence. By pipeline parameters, we mean the functions being used to calculate segmentation, probability estimation, confirmation measure and aggregation as shown in figure 1 in this paper.



In [6]:

    
print(goodcm)









    



Coherence_Measure(seg=<function s_one_pre at 0x7f6b0a12ed90>, prob=<function p_boolean_document at 0x7f6b0a12eea0>, conf=<function log_conditional_probability at 0x7f6b09c326a8>, aggr=<function arithmetic_mean at 0x7f6b09c32f28>)

Interpreting the topics

As we will see below using LDA visualization, the better model comes up with two topics composed of the following words:

goodLdaModel:
- Topic 1: More weightage assigned to words such as "system", "user", "eps", "interface" etc which captures the first set of documents.
- Topic 2: More weightage assigned to words such as "graph", "trees", "survey" which captures the topic in the second set of documents.
badLdaModel:
- Topic 1: More weightage assigned to words such as "system", "user", "trees", "graph" which doesn't make the topic clear enough.
- Topic 2: More weightage assigned to words such as "system", "trees", "graph", "user" which is similar to the first topic. Hence both topics are not human-interpretable.

Therefore, the topic coherence for the goodLdaModel should be greater for this than the badLdaModel since the topics it comes up with are more human-interpretable. We will see this using u_mass and c_v topic coherence measures.

Visualize topic models



In [7]:

    
if CAN_VISUALIZE:
    prepared = pyLDAvis.gensim.prepare(goodLdaModel, corpus, dictionary)
    display(pyLDAvis.display(prepared))



In [8]:

    
if CAN_VISUALIZE:
    prepared = pyLDAvis.gensim.prepare(badLdaModel, corpus, dictionary)
    display(pyLDAvis.display(prepared))



In [9]:

    
print(goodcm.get_coherence())
print(badcm.get_coherence())









    



-14.695344054692296
-14.722989402972397

Using C_V coherence



In [10]:

    
goodcm = CoherenceModel(model=goodLdaModel, texts=texts, dictionary=dictionary, coherence='c_v')
badcm = CoherenceModel(model=badLdaModel, texts=texts, dictionary=dictionary, coherence='c_v')

Pipeline parameters for C_V coherence



In [11]:

    
print(goodcm)









    



Coherence_Measure(seg=<function s_one_set at 0x7f6b0a12ee18>, prob=<function p_boolean_sliding_window at 0x7f6b0a1421e0>, conf=<function cosine_similarity at 0x7f6b09c328c8>, aggr=<function arithmetic_mean at 0x7f6b09c32f28>)

Print coherence values



In [12]:

    
print(goodcm.get_coherence())
print(badcm.get_coherence())









    



0.3838413553737203
0.3838413553737203

Support for wrappers

This API supports gensim's ldavowpalwabbit and ldamallet wrappers as input parameter to model.



In [13]:

    
# Replace with path to your Vowpal Wabbit installation
vw_path = '/usr/local/bin/vw'

# Replace with path to your Mallet installation
home = os.path.expanduser('~')
mallet_path = os.path.join(home, 'mallet-2.0.8', 'bin', 'mallet')



In [14]:

    
model1 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=50)
model2 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=1)









    



---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-14-9421c07a3fe9> in <module>
----> 1 model1 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=50)
      2 model2 = LdaVowpalWabbit(vw_path, corpus=corpus, num_topics=2, id2word=dictionary, passes=1)

~/git/gensim/gensim/models/wrappers/ldavowpalwabbit.py in __init__(self, vw_path, corpus, num_topics, id2word, chunksize, passes, alpha, eta, decay, offset, gamma_threshold, random_seed, cleanup_files, tmp_prefix)
    214 
    215         if corpus is not None:
--> 216             self.train(corpus)
    217 
    218     def train(self, corpus):

~/git/gensim/gensim/models/wrappers/ldavowpalwabbit.py in train(self, corpus)
    235         cmd = self._get_vw_train_command(corpus_size)
    236 
--> 237         _run_vw_command(cmd)
    238 
    239         # ensure that future updates of this model use correct offset

~/git/gensim/gensim/models/wrappers/ldavowpalwabbit.py in _run_vw_command(cmd)
    849     logger.info("Running Vowpal Wabbit command: %s", ' '.join(cmd))
    850     proc = subprocess.Popen(cmd, stdout=subprocess.PIPE,
--> 851                             stderr=subprocess.STDOUT)
    852     output = proc.communicate()[0].decode('utf-8')
    853     logger.debug("Vowpal Wabbit output: %s", output)

/usr/lib/python3.7/subprocess.py in __init__(self, args, bufsize, executable, stdin, stdout, stderr, preexec_fn, close_fds, shell, cwd, env, universal_newlines, startupinfo, creationflags, restore_signals, start_new_session, pass_fds, encoding, errors, text)
    773                                 c2pread, c2pwrite,
    774                                 errread, errwrite,
--> 775                                 restore_signals, start_new_session)
    776         except:
    777             # Cleanup if the child failed starting.

/usr/lib/python3.7/subprocess.py in _execute_child(self, args, executable, preexec_fn, close_fds, pass_fds, cwd, env, startupinfo, creationflags, shell, p2cread, p2cwrite, c2pread, c2pwrite, errread, errwrite, restore_signals, start_new_session)
   1520                         if errno_num == errno.ENOENT:
   1521                             err_msg += ': ' + repr(err_filename)
-> 1522                     raise child_exception_type(errno_num, err_msg, err_filename)
   1523                 raise child_exception_type(err_msg)
   1524 

FileNotFoundError: [Errno 2] No such file or directory: '/usr/local/bin/vw': '/usr/local/bin/vw'



In [ ]:

    
cm1 = CoherenceModel(model=model1, corpus=corpus, coherence='u_mass')
cm2 = CoherenceModel(model=model2, corpus=corpus, coherence='u_mass')
print(cm1.get_coherence())
print(cm2.get_coherence())



In [ ]:

    
model1 = LdaMallet(mallet_path, corpus=corpus, num_topics=2, id2word=dictionary, iterations=50)
model2 = LdaMallet(mallet_path, corpus=corpus, num_topics=2, id2word=dictionary, iterations=1)



In [ ]:

    
cm1 = CoherenceModel(model=model1, texts=texts, coherence='c_v')
cm2 = CoherenceModel(model=model2, texts=texts, coherence='c_v')
print(cm1.get_coherence())
print(cm2.get_coherence())

Support for other topic models

The gensim topics coherence pipeline can be used with other topics models too. Only the tokenized topics should be made available for the pipeline. Eg. with the gensim HDP model



In [ ]:

    
hm = HdpModel(corpus=corpus, id2word=dictionary)



In [ ]:

    
# To get the topic words from the model
topics = []
for topic_id, topic in hm.show_topics(num_topics=10, formatted=False):
    topic = [word for word, _ in topic]
    topics.append(topic)
topics[:2]



In [ ]:

    
# Initialize CoherenceModel using `topics` parameter
cm = CoherenceModel(topics=topics, corpus=corpus, dictionary=dictionary, coherence='u_mass')
cm.get_coherence()

Conclusion

Hence as we can see, the u_mass and c_v coherence for the good LDA model is much more (better) than that for the bad LDA model. This is because, simply, the good LDA model usually comes up with better topics that are more human interpretable. The badLdaModel however fails to decipher between these two topics and comes up with topics which are not clear to a human. The u_mass and c_v topic coherences capture this wonderfully by giving the interpretability of these topics a number as we can see above. Hence this coherence measure can be used to compare difference topic models based on their human-interpretability.