<reponame>ishatserka/MachineLearningAndDataAnalysisCoursera<gh_stars>1-10
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010 <NAME> <<EMAIL>>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

"""
Automated tests for checking transformation algorithms (the models package).
"""

import logging
import os
import tempfile
import unittest

import numpy as np

from gensim.corpora.dictionary import Dictionary
from gensim.matutils import argsort
from gensim.models.coherencemodel import CoherenceModel, boolean_document_based
from gensim.models.ldamodel import LdaModel
from gensim.models.wrappers import LdaMallet
from gensim.models.wrappers import LdaVowpalWabbit


def testfile():
    # temporary data will be stored to this file
    return os.path.join(tempfile.gettempdir(), 'gensim_models.tst')


class TestCoherenceModel(unittest.TestCase):

    # set up vars used in testing ("Deerwester" from the web tutorial)
    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']
    ]
    dictionary = Dictionary(texts)

    @classmethod
    def setUpClass(cls):
        cls.corpus = [cls.dictionary.doc2bow(text) for text in cls.texts]

    def setUp(self):
        # Suppose given below are the topics which two different LdaModels come up with.
        # `topics1` is clearly better as it has a clear distinction between system-human
        # interaction and graphs. Hence both the coherence measures for `topics1` should be
        # greater.
        self.topics1 = [['human', 'computer', 'system', 'interface'],
                        ['graph', 'minors', 'trees', 'eps']]
        self.topics2 = [['user', 'graph', 'minors', 'system'],
                        ['time', 'graph', 'survey', 'minors']]
        self.ldamodel = LdaModel(
            corpus=self.corpus, id2word=self.dictionary, num_topics=2,
            passes=0, iterations=0)

        mallet_home = os.environ.get('MALLET_HOME', None)
        self.mallet_path = os.path.join(mallet_home, 'bin', 'mallet') if mallet_home else None
        if self.mallet_path:
            self.malletmodel = LdaMallet(
                mallet_path=self.mallet_path, corpus=self.corpus,
                id2word=self.dictionary, num_topics=2, iterations=0)

        vw_path = os.environ.get('VOWPAL_WABBIT_PATH', None)
        if not vw_path:
            logging.info(
                "Environment variable 'VOWPAL_WABBIT_PATH' not specified,"
                " skipping sanity checks for LDA Model")
            self.vw_path = None
        else:
            self.vw_path = vw_path
            self.vwmodel = LdaVowpalWabbit(
                self.vw_path, corpus=self.corpus, id2word=self.dictionary,
                num_topics=2, passes=0)

    def check_coherence_measure(self, coherence):
        """Check provided topic coherence algorithm on given topics"""
        if coherence in boolean_document_based:
            kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence=coherence)
        else:
            kwargs = dict(texts=self.texts, dictionary=self.dictionary, coherence=coherence)

        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm2 = CoherenceModel(topics=self.topics2, **kwargs)
        self.assertGreater(cm1.get_coherence(), cm2.get_coherence())

    def testUMass(self):
        """Test U_Mass topic coherence algorithm on given topics"""
        self.check_coherence_measure('u_mass')

    def testCv(self):
        """Test C_v topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_v')

    def testCuci(self):
        """Test C_uci topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_uci')

    def testCnpmi(self):
        """Test C_npmi topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_npmi')

    def testUMassLdaModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA Model"""
        # Note that this is just a sanity check because LDA does not guarantee a better coherence
        # value on the topics if iterations are increased. This can be seen here:
        # https://gist.github.com/dsquareindia/60fd9ab65b673711c3fa00509287ddde
        CoherenceModel(model=self.ldamodel, corpus=self.corpus, coherence='u_mass')

    def testCvLdaModel(self):
        """Perform sanity check to see if c_v coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_v')

    def testCuciLdaModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_uci')

    def testCnpmiLdaModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_npmi')

    def testUMassMalletModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, corpus=self.corpus, coherence='u_mass')

    def testCvMalletModel(self):
        """Perform sanity check to see if c_v coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_v')

    def testCuciMalletModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_uci')

    def testCnpmiMalletModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_npmi')

    def testUMassVWModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, corpus=self.corpus, coherence='u_mass')

    def testCvVWModel(self):
        """Perform sanity check to see if c_v coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_v')

    def testCuciVWModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_uci')

    def testCnpmiVWModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_npmi')

    def testErrors(self):
        """Test if errors are raised on bad input"""
        # not providing dictionary
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
            coherence='u_mass')
        # not providing texts for c_v and instead providing corpus
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
            dictionary=self.dictionary, coherence='c_v')
        # not providing corpus or texts for u_mass
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, dictionary=self.dictionary,
            coherence='u_mass')

    def testPersistence(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceCompressed(self):
        fname = testfile() + '.gz'
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceAfterProbabilityEstimationUsingCorpus(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.estimate_probabilities()
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertIsNotNone(model2._accumulator)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceAfterProbabilityEstimationUsingTexts(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, texts=self.texts, dictionary=self.dictionary, coherence='c_v')
        model.estimate_probabilities()
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertIsNotNone(model2._accumulator)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testAccumulatorCachingSameSizeTopics(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        accumulator = cm1._accumulator
        self.assertIsNotNone(accumulator)
        cm1.topics = self.topics1
        self.assertEqual(accumulator, cm1._accumulator)
        cm1.topics = self.topics2
        self.assertEqual(None, cm1._accumulator)

    def testAccumulatorCachingTopicSubsets(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        accumulator = cm1._accumulator
        self.assertIsNotNone(accumulator)
        cm1.topics = [t[:2] for t in self.topics1]
        self.assertEqual(accumulator, cm1._accumulator)
        cm1.topics = self.topics1
        self.assertEqual(accumulator, cm1._accumulator)

    def testAccumulatorCachingWithModelSetting(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        self.assertIsNotNone(cm1._accumulator)
        cm1.model = self.ldamodel
        topics = []
        for topic in self.ldamodel.state.get_lambda():
            bestn = argsort(topic, topn=cm1.topn, reverse=True)
            topics.append(bestn)
        self.assertTrue(np.array_equal(topics, cm1.topics))
        self.assertIsNone(cm1._accumulator)


if __name__ == '__main__':
    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
    unittest.main()

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2010 <NAME> <<EMAIL>>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html

"""
Automated tests for checking transformation algorithms (the models package).
"""

import logging
import os
import tempfile
import unittest

import numpy as np

from gensim.corpora.dictionary import Dictionary
from gensim.matutils import argsort
from gensim.models.coherencemodel import CoherenceModel, boolean_document_based
from gensim.models.ldamodel import LdaModel
from gensim.models.wrappers import LdaMallet
from gensim.models.wrappers import LdaVowpalWabbit


def testfile():
    # temporary data will be stored to this file
    return os.path.join(tempfile.gettempdir(), 'gensim_models.tst')


class TestCoherenceModel(unittest.TestCase):

    # set up vars used in testing ("Deerwester" from the web tutorial)
    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']
    ]
    dictionary = Dictionary(texts)

    @classmethod
    def setUpClass(cls):
        cls.corpus = [cls.dictionary.doc2bow(text) for text in cls.texts]

    def setUp(self):
        # Suppose given below are the topics which two different LdaModels come up with.
        # `topics1` is clearly better as it has a clear distinction between system-human
        # interaction and graphs. Hence both the coherence measures for `topics1` should be
        # greater.
        self.topics1 = [['human', 'computer', 'system', 'interface'],
                        ['graph', 'minors', 'trees', 'eps']]
        self.topics2 = [['user', 'graph', 'minors', 'system'],
                        ['time', 'graph', 'survey', 'minors']]
        self.ldamodel = LdaModel(
            corpus=self.corpus, id2word=self.dictionary, num_topics=2,
            passes=0, iterations=0)

        mallet_home = os.environ.get('MALLET_HOME', None)
        self.mallet_path = os.path.join(mallet_home, 'bin', 'mallet') if mallet_home else None
        if self.mallet_path:
            self.malletmodel = LdaMallet(
                mallet_path=self.mallet_path, corpus=self.corpus,
                id2word=self.dictionary, num_topics=2, iterations=0)

        vw_path = os.environ.get('VOWPAL_WABBIT_PATH', None)
        if not vw_path:
            logging.info(
                "Environment variable 'VOWPAL_WABBIT_PATH' not specified,"
                " skipping sanity checks for LDA Model")
            self.vw_path = None
        else:
            self.vw_path = vw_path
            self.vwmodel = LdaVowpalWabbit(
                self.vw_path, corpus=self.corpus, id2word=self.dictionary,
                num_topics=2, passes=0)

    def check_coherence_measure(self, coherence):
        """Check provided topic coherence algorithm on given topics"""
        if coherence in boolean_document_based:
            kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence=coherence)
        else:
            kwargs = dict(texts=self.texts, dictionary=self.dictionary, coherence=coherence)

        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm2 = CoherenceModel(topics=self.topics2, **kwargs)
        self.assertGreater(cm1.get_coherence(), cm2.get_coherence())

    def testUMass(self):
        """Test U_Mass topic coherence algorithm on given topics"""
        self.check_coherence_measure('u_mass')

    def testCv(self):
        """Test C_v topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_v')

    def testCuci(self):
        """Test C_uci topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_uci')

    def testCnpmi(self):
        """Test C_npmi topic coherence algorithm on given topics"""
        self.check_coherence_measure('c_npmi')

    def testUMassLdaModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA Model"""
        # Note that this is just a sanity check because LDA does not guarantee a better coherence
        # value on the topics if iterations are increased. This can be seen here:
        # https://gist.github.com/dsquareindia/60fd9ab65b673711c3fa00509287ddde
        CoherenceModel(model=self.ldamodel, corpus=self.corpus, coherence='u_mass')

    def testCvLdaModel(self):
        """Perform sanity check to see if c_v coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_v')

    def testCuciLdaModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_uci')

    def testCnpmiLdaModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA Model"""
        CoherenceModel(model=self.ldamodel, texts=self.texts, coherence='c_npmi')

    def testUMassMalletModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, corpus=self.corpus, coherence='u_mass')

    def testCvMalletModel(self):
        """Perform sanity check to see if c_v coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_v')

    def testCuciMalletModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_uci')

    def testCnpmiMalletModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA Mallet gensim wrapper"""
        if not self.mallet_path:
            return
        CoherenceModel(model=self.malletmodel, texts=self.texts, coherence='c_npmi')

    def testUMassVWModel(self):
        """Perform sanity check to see if u_mass coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, corpus=self.corpus, coherence='u_mass')

    def testCvVWModel(self):
        """Perform sanity check to see if c_v coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_v')

    def testCuciVWModel(self):
        """Perform sanity check to see if c_uci coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_uci')

    def testCnpmiVWModel(self):
        """Perform sanity check to see if c_npmi coherence works with LDA VW gensim wrapper"""
        if not self.vw_path:
            return
        CoherenceModel(model=self.vwmodel, texts=self.texts, coherence='c_npmi')

    def testErrors(self):
        """Test if errors are raised on bad input"""
        # not providing dictionary
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
            coherence='u_mass')
        # not providing texts for c_v and instead providing corpus
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, corpus=self.corpus,
            dictionary=self.dictionary, coherence='c_v')
        # not providing corpus or texts for u_mass
        self.assertRaises(
            ValueError, CoherenceModel, topics=self.topics1, dictionary=self.dictionary,
            coherence='u_mass')

    def testPersistence(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceCompressed(self):
        fname = testfile() + '.gz'
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceAfterProbabilityEstimationUsingCorpus(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        model.estimate_probabilities()
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertIsNotNone(model2._accumulator)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testPersistenceAfterProbabilityEstimationUsingTexts(self):
        fname = testfile()
        model = CoherenceModel(
            topics=self.topics1, texts=self.texts, dictionary=self.dictionary, coherence='c_v')
        model.estimate_probabilities()
        model.save(fname)
        model2 = CoherenceModel.load(fname)
        self.assertIsNotNone(model2._accumulator)
        self.assertTrue(model.get_coherence() == model2.get_coherence())

    def testAccumulatorCachingSameSizeTopics(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        accumulator = cm1._accumulator
        self.assertIsNotNone(accumulator)
        cm1.topics = self.topics1
        self.assertEqual(accumulator, cm1._accumulator)
        cm1.topics = self.topics2
        self.assertEqual(None, cm1._accumulator)

    def testAccumulatorCachingTopicSubsets(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        accumulator = cm1._accumulator
        self.assertIsNotNone(accumulator)
        cm1.topics = [t[:2] for t in self.topics1]
        self.assertEqual(accumulator, cm1._accumulator)
        cm1.topics = self.topics1
        self.assertEqual(accumulator, cm1._accumulator)

    def testAccumulatorCachingWithModelSetting(self):
        kwargs = dict(corpus=self.corpus, dictionary=self.dictionary, coherence='u_mass')
        cm1 = CoherenceModel(topics=self.topics1, **kwargs)
        cm1.estimate_probabilities()
        self.assertIsNotNone(cm1._accumulator)
        cm1.model = self.ldamodel
        topics = []
        for topic in self.ldamodel.state.get_lambda():
            bestn = argsort(topic, topn=cm1.topn, reverse=True)
            topics.append(bestn)
        self.assertTrue(np.array_equal(topics, cm1.topics))
        self.assertIsNone(cm1._accumulator)


if __name__ == '__main__':
    logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.DEBUG)
    unittest.main()
#!/usr/bin/env python # -*- coding: utf-8 -*- # # Copyright (C) 2010 <NAME> <<EMAIL>> # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html Automated tests for checking transformation algorithms (the models package). # temporary data will be stored to this file # set up vars used in testing ("Deerwester" from the web tutorial) # Suppose given below are the topics which two different LdaModels come up with. # `topics1` is clearly better as it has a clear distinction between system-human # interaction and graphs. Hence both the coherence measures for `topics1` should be # greater. Check provided topic coherence algorithm on given topics Test U_Mass topic coherence algorithm on given topics Test C_v topic coherence algorithm on given topics Test C_uci topic coherence algorithm on given topics Test C_npmi topic coherence algorithm on given topics Perform sanity check to see if u_mass coherence works with LDA Model # Note that this is just a sanity check because LDA does not guarantee a better coherence # value on the topics if iterations are increased. This can be seen here: # https://gist.github.com/dsquareindia/60fd9ab65b673711c3fa00509287ddde Perform sanity check to see if c_v coherence works with LDA Model Perform sanity check to see if c_uci coherence works with LDA Model Perform sanity check to see if c_npmi coherence works with LDA Model Perform sanity check to see if u_mass coherence works with LDA Mallet gensim wrapper Perform sanity check to see if c_v coherence works with LDA Mallet gensim wrapper Perform sanity check to see if c_uci coherence works with LDA Mallet gensim wrapper Perform sanity check to see if c_npmi coherence works with LDA Mallet gensim wrapper Perform sanity check to see if u_mass coherence works with LDA VW gensim wrapper Perform sanity check to see if c_v coherence works with LDA VW gensim wrapper Perform sanity check to see if c_uci coherence works with LDA VW gensim wrapper Perform sanity check to see if c_npmi coherence works with LDA VW gensim wrapper Test if errors are raised on bad input # not providing dictionary # not providing texts for c_v and instead providing corpus # not providing corpus or texts for u_mass