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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (C) 2010 Radim Rehurek <radimrehurek@seznam.cz> | ||
| # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | ||
|
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| """ | ||
| Module for calculating topic coherence in python. This is the implementation of | ||
| the four stage topic coherence pipeline from the paper [1]. | ||
| The four stage pipeline is basically: | ||
| Segmentation -> Probability Estimation -> Confirmation Measure -> Aggregation. | ||
| Implementation of this pipeline allows for the user to in essence "make" a | ||
| coherence measure of his/her choice by choosing a method in each of the pipelines. | ||
| [1] Michael Roeder, Andreas Both and Alexander Hinneburg. Exploring the space of topic | ||
| coherence measures. http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf. | ||
| """ | ||
|
|
||
| import logging | ||
|
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||
| from gensim import interfaces | ||
| from gensim.topic_coherence import (segmentation, probability_estimation, | ||
| direct_confirmation_measure, indirect_confirmation_measure, | ||
| aggregation) | ||
| from gensim.corpora import Dictionary | ||
| from gensim.matutils import argsort | ||
| from gensim.utils import is_corpus | ||
| from gensim.models.ldamodel import LdaModel | ||
| from gensim.models.wrappers import LdaVowpalWabbit | ||
|
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||
| logger = logging.getLogger(__name__) | ||
|
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|
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| class CoherenceModel(interfaces.TransformationABC): | ||
| """ | ||
| Objects of this class allow for building and maintaining a model for topic | ||
| coherence. | ||
| The main methods are: | ||
| 1. constructor, which initializes the four stage pipeline by accepting a coherence measure, | ||
| 2. the ``get_coherence()`` method, which returns the topic coherence. | ||
| >>> cm = CoherenceModel(model=tm, corpus=corpus, coherence='u_mass') # tm is the trained topic model | ||
| >>> cm.get_coherence() | ||
| Model persistency is achieved via its load/save methods. | ||
| """ | ||
| def __init__(self, model, texts=None, corpus=None, dictionary=None, coherence='c_v'): | ||
| """ | ||
| Args: | ||
| ---- | ||
| model : Pre-trained topic model. | ||
| texts : Tokenized texts. Needed for coherence models that use sliding window based probability estimator. | ||
| corpus : Gensim document corpus. | ||
| dictionary : Gensim dictionary mapping of id word to create corpus. | ||
| coherence : Coherence measure to be used. Supported values are: | ||
| u_mass | ||
| c_v | ||
| """ | ||
| if texts is None and corpus is None: | ||
| raise ValueError("One of texts or corpus has to be provided.") | ||
| if coherence == 'u_mass': | ||
| if is_corpus(corpus)[0]: | ||
| if dictionary is None: | ||
| if model.id2word[0] == 0: | ||
| raise ValueError("The associated dictionary should be provided with the corpus or 'id2word' for topic model" | ||
| "should be set as the dictionary.") | ||
| else: | ||
| self.dictionary = model.id2word | ||
| else: | ||
| self.dictionary = dictionary | ||
| self.corpus = corpus | ||
| elif texts is not None: | ||
| self.texts = texts | ||
| if dictionary is None: | ||
| self.dictionary = Dictionary(self.texts) | ||
| else: | ||
| self.dictionary = dictionary | ||
| self.corpus = [self.dictionary.doc2bow(text) for text in self.texts] | ||
| else: | ||
| raise ValueError("Either 'corpus' with 'dictionary' or 'texts' should be provided for %s coherence." % coherence) | ||
|
|
||
| elif coherence == 'c_v': | ||
| if texts is None: | ||
| raise ValueError("'texts' should be provided for %s coherence." % coherence) | ||
| else: | ||
| self.texts = texts | ||
| self.dictionary = Dictionary(self.texts) | ||
| self.corpus = [self.dictionary.doc2bow(text) for text in self.texts] | ||
|
|
||
| else: | ||
| raise ValueError("%s coherence is not currently supported." % coherence) | ||
|
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| self.model = model | ||
| self.topics = self._get_topics() | ||
| self.coherence = coherence | ||
| # Set pipeline parameters: | ||
| if self.coherence == 'u_mass': | ||
| self.seg = segmentation.s_one_pre | ||
| self.prob = probability_estimation.p_boolean_document | ||
| self.conf = direct_confirmation_measure.log_conditional_probability | ||
| self.aggr = aggregation.arithmetic_mean | ||
|
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||
| elif self.coherence == 'c_v': | ||
| self.seg = segmentation.s_one_set | ||
| self.prob = probability_estimation.p_boolean_sliding_window | ||
| self.conf = indirect_confirmation_measure.cosine_similarity | ||
| self.aggr = aggregation.arithmetic_mean | ||
|
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||
| def __str__(self): | ||
| return "CoherenceModel(segmentation=%s, probability estimation=%s, confirmation measure=%s, aggregation=%s)" % ( | ||
| self.seg, self.prob, self.conf, self.aggr) | ||
|
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||
| def _get_topics(self): | ||
| """Internal helper function to return topics from a trained topic model.""" | ||
| topics = [] # FIXME : Meant to work for LDAModel, LdaVowpalWabbit right now. Make it work for others. | ||
| if isinstance(self.model, LdaModel): | ||
| for topic in self.model.state.get_lambda(): | ||
| bestn = argsort(topic, topn=10, reverse=True) | ||
| topics.append(bestn) | ||
| elif isinstance(self.model, LdaVowpalWabbit): | ||
| for topic in self.model._get_topics(): | ||
| bestn = argsort(topic, topn=10, reverse=True) | ||
| topics.append(bestn) | ||
| return topics | ||
|
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||
| def get_coherence(self): | ||
| if self.coherence == 'u_mass': | ||
| segmented_topics = self.seg(self.topics) | ||
| per_topic_postings, num_docs = self.prob(self.corpus, segmented_topics) | ||
| confirmed_measures = self.conf(segmented_topics, per_topic_postings, num_docs) | ||
| return self.aggr(confirmed_measures) | ||
|
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||
| elif self.coherence == 'c_v': | ||
| segmented_topics = self.seg(self.topics) | ||
| per_topic_postings, num_windows = self.prob(texts=self.texts, segmented_topics=segmented_topics, | ||
| dictionary=self.dictionary, window_size=2) # FIXME : Change window size to 110 finally. | ||
| confirmed_measures = self.conf(self.topics, segmented_topics, per_topic_postings, 'nlr', 1, num_windows) | ||
| return self.aggr(confirmed_measures) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz> | ||
| # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | ||
|
|
||
| """ | ||
| Automated tests for direct confirmation measures in the direct_confirmation_measure module. | ||
| """ | ||
|
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||
| import logging | ||
| import unittest | ||
|
|
||
| from gensim.topic_coherence import direct_confirmation_measure | ||
|
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||
| class TestDirectConfirmationMeasure(unittest.TestCase): | ||
| def setUp(self): | ||
| # Set up toy example for better understanding and testing | ||
| # of this module. See the modules for the mathematical formulas | ||
| self.segmentation = [[(1, 2)]] | ||
| self.posting_list = {1: set([2, 3, 4]), 2: set([3, 5])} | ||
| self.num_docs = 5 | ||
|
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||
| def testLogConditionalProbability(self): | ||
| """Test log_conditional_probability()""" | ||
| obtained = direct_confirmation_measure.log_conditional_probability(self.segmentation, self.posting_list, self.num_docs)[0] | ||
| # Answer should be ~ ln(1 / 2) = -0.693147181 | ||
| expected = -0.693147181 | ||
| self.assertAlmostEqual(obtained, expected) | ||
|
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||
| def testLogRatioMeasure(self): | ||
| """Test log_ratio_measure()""" | ||
| obtained = direct_confirmation_measure.log_ratio_measure(self.segmentation, self.posting_list, self.num_docs)[0] | ||
| # Answer should be ~ ln{(1 / 5) / [(3 / 5) * (2 / 5)]} = -0.182321557 | ||
| expected = -0.182321557 | ||
| self.assertAlmostEqual(obtained, expected) | ||
|
|
||
| def testNormalizedLogRatioMeasure(self): | ||
| """Test normalized_log_ratio_measure()""" | ||
| obtained = direct_confirmation_measure.normalized_log_ratio_measure(self.segmentation, self.posting_list, self.num_docs)[0] | ||
| # Answer should be ~ -0.182321557 / ln(1 / 5) = 0.113282753 | ||
| expected = 0.113282753 | ||
| self.assertAlmostEqual(obtained, expected) | ||
|
|
||
| if __name__ == '__main__': | ||
| logging.root.setLevel(logging.WARNING) | ||
| unittest.main() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,49 @@ | ||
| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz> | ||
| # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | ||
|
|
||
| """ | ||
| Automated tests for indirect confirmation measures in the indirect_confirmation_measure module. | ||
| """ | ||
|
|
||
| import logging | ||
| import unittest | ||
|
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| from gensim.topic_coherence import indirect_confirmation_measure | ||
|
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| import numpy as np | ||
| from numpy import array | ||
|
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| class TestIndirectConfirmation(unittest.TestCase): | ||
| def setUp(self): | ||
| # Set up toy example for better understanding and testing | ||
| # of this module. See the modules for the mathematical formulas | ||
| self.topics = [np.array([1, 2])] | ||
| # Result from s_one_set segmentation: | ||
| self.segmentation = [[(1, array([1, 2])), (2, array([1, 2]))]] | ||
| self.posting_list = {1: set([2, 3, 4]), 2: set([3, 5])} | ||
| self.gamma = 1 | ||
| self.measure = 'nlr' | ||
| self.num_docs = 5 | ||
|
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||
| def testCosineSimilarity(self): | ||
| """Test cosine_similarity()""" | ||
| obtained = indirect_confirmation_measure.cosine_similarity(self.topics, self.segmentation, | ||
| self.posting_list, self.measure, | ||
| self.gamma, self.num_docs) | ||
| # The steps involved in this calculation are as follows: | ||
| # 1. Take (1, array([1, 2]). Take w' which is 1. | ||
| # 2. Calculate nlr(1, 1), nlr(1, 2). This is our first vector. | ||
| # 3. Take w* which is array([1, 2]). | ||
| # 4. Calculate nlr(1, 1) + nlr(2, 1). Calculate nlr(1, 2), nlr(2, 2). This is our second vector. | ||
| # 5. Find out cosine similarity between these two vectors. | ||
| # 6. Similarly for the second segmentation. | ||
| expected = [0.6230, 0.6230] # To account for EPSILON approximation | ||
| self.assertAlmostEqual(obtained[0], expected[0], 4) | ||
| self.assertAlmostEqual(obtained[1], expected[1], 4) | ||
|
|
||
| if __name__ == '__main__': | ||
| logging.root.setLevel(logging.WARNING) | ||
| unittest.main() |
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