def test_new_corpus(self):
"""
Test if the bow representation of new corpus is consistent
:return:
:rtype:
"""
other_texts_without_unseen_word = [["computer", "time", "graph"], ["survey", "response", "eps"], ["human", "system", "computer"]]
other_corpus_without_unseen_word = [common_dictionary.doc2bow(text) for text in other_texts_without_unseen_word]
self.assertEqual(other_corpus_without_unseen_word[0], [(0, 1), (6, 1), (10, 1)])
other_texts_with_unseen_word = [["computer", "graph", "hardware", "time", ], ["survey", "response", "eps", "administrator"]]
other_corpus_with_unseen_word = [common_dictionary.doc2bow(text) for text in other_texts_with_unseen_word]
self.assertEqual(other_corpus_with_unseen_word[0], [(0, 1), (6, 1), (10, 1)])
def testEmptyDocument(self):
local_texts = common_texts + [['only_occurs_once_in_corpus_and_alone_in_doc']]
dictionary = Dictionary(local_texts)
dictionary.filter_extremes(no_below=2)
corpus = [dictionary.doc2bow(text) for text in local_texts]
a2d = author2doc.copy()
a2d['joaquin'] = [len(local_texts) - 1]
self.class_(corpus, author2doc=a2d, id2word=dictionary, num_topics=2)
def setUp(self):
self.cls = similarities.SoftCosineSimilarity
self.dictionary = Dictionary(texts)
self.corpus = [dictionary.doc2bow(document) for document in texts]
similarity_matrix = scipy.sparse.identity(12, format="lil")
similarity_matrix[dictionary.token2id["user"],
dictionary.token2id["human"]] = 0.5
similarity_matrix[dictionary.token2id["human"],
dictionary.token2id["user"]] = 0.5
self.similarity_matrix = similarity_matrix.tocsc()
def testEmptyDocument(self):
local_texts = common_texts + [[
'only_occurs_once_in_corpus_and_alone_in_doc'
]]
dictionary = Dictionary(local_texts)
dictionary.filter_extremes(no_below=2)
corpus = [dictionary.doc2bow(text) for text in local_texts]
a2d = author2doc.copy()
a2d['joaquin'] = [len(local_texts) - 1]
self.class_(corpus, author2doc=a2d, id2word=dictionary, num_topics=2)
def test_lda_update_2(self):
"""
Update with unseen text, which have new words.
I add 'hardware', 'administrator' to test.
:return:
:rtype:
"""
other_texts = [["computer", "graph", "hardware", "time", ], ["survey", "response", "eps", "administrator"]]
other_corpus = [common_dictionary.doc2bow(text) for text in other_texts]
original_model = copy.deepcopy(self.lda)
self.lda.update(other_corpus)
self.assertEqual(self.lda.id2word.token2id, original_model.id2word.token2id)
def testNonIncreasing(self):
""" Check that similarities are non-increasing when `num_best` is not `None`."""
# NOTE: this could be implemented for other similarities as well (i.e. in _TestSimilarityABC).
index = self.cls(corpus, self.similarity_matrix, num_best=5)
query = dictionary.doc2bow(texts[0])
sims = index[query]
sims2 = numpy.asarray(sims)[:, 1] # Just the similarities themselves.
# The difference of adjacent elements should be negative.
cond = sum(numpy.diff(sims2) < 0) == len(sims2) - 1
self.assertTrue(cond)
def testNonIncreasing(self):
""" Check that similarities are non-increasing when `num_best` is not `None`."""
# NOTE: this could be implemented for other similarities as well (i.e. in _TestSimilarityABC).
index = self.cls(corpus, self.similarity_matrix, num_best=5)
query = dictionary.doc2bow(texts[0])
sims = index[query]
sims2 = numpy.asarray(sims)[:, 1] # Just the similarities themselves.
# The difference of adjacent elements should be less than or equal to zero.
cond = sum(numpy.diff(sims2) <= 0) == len(sims2) - 1
self.assertTrue(cond)
def test_lda_update_1(self):
"""
Update with unseen text, which doesn't have new words.
:return:
:rtype:
"""
other_texts = [["computer", "time", "graph"], ["survey", "response", "eps"], ["human", "system", "computer"]]
other_corpus = [common_dictionary.doc2bow(text) for text in other_texts]
original_model = copy.deepcopy(self.lda)
# Inplace update
self.lda.update(other_corpus)
self.assertNotEqual(self.lda, original_model)
self.assertEqual(self.lda.id2word.token2id, original_model.id2word.token2id)
def testChunking(self):
# Override testChunking.
index = self.cls(corpus, self.similarity_matrix)
query = [dictionary.doc2bow(document) for document in texts[:3]]
sims = index[query]
for i in range(3):
self.assertTrue(numpy.alltrue(sims[i, i] == 1.0)) # Similarity of a document with itself is 1.0.
# test the same thing but with num_best
index.num_best = 5
sims = index[query]
for i, chunk in enumerate(sims):
expected = i
self.assertAlmostEqual(expected, chunk[0][0], places=2)
expected = 1.0
self.assertAlmostEqual(expected, chunk[0][1], places=2)
def testChunking(self):
# Override testChunking.
index = self.cls(corpus, self.similarity_matrix)
query = [dictionary.doc2bow(document) for document in texts[:3]]
sims = index[query]
for i in range(3):
self.assertTrue(numpy.alltrue(sims[i, i] == 1.0)) # Similarity of a document with itself is 1.0.
# test the same thing but with num_best
index.num_best = 5
sims = index[query]
for i, chunk in enumerate(sims):
expected = i
self.assertAlmostEqual(expected, chunk[0][0], places=2)
expected = 1.0
self.assertAlmostEqual(expected, chunk[0][1], places=2)
def testFull(self, num_best=None):
# Override testFull.
# Single query
index = self.cls(corpus, self.similarity_matrix, num_best=num_best)
query = dictionary.doc2bow(texts[0])
sims = index[query]
if num_best is not None:
# Sparse array.
for i, sim in sims:
self.assertTrue(numpy.alltrue(sim <= 1.0))
self.assertTrue(numpy.alltrue(sim >= 0.0))
else:
self.assertAlmostEqual(
1.0, sims[0]) # Similarity of a document with itself is 1.0.
self.assertTrue(numpy.alltrue(sims[1:] >= 0.0))
self.assertTrue(numpy.alltrue(sims[1:] < 1.0))
expected = 2.1889350195476758
self.assertAlmostEqual(expected, numpy.sum(sims))
# Corpora
for query in (
corpus, # Basic text corpus.
self.tfidf[corpus]
): # Transformed corpus without slicing support.
index = self.cls(query, self.similarity_matrix, num_best=num_best)
sims = index[query]
if num_best is not None:
# Sparse array.
for result in sims:
for i, sim in result:
self.assertTrue(numpy.alltrue(sim <= 1.0))
self.assertTrue(numpy.alltrue(sim >= 0.0))
else:
for i, result in enumerate(sims):
self.assertAlmostEqual(
1.0, result[i]
) # Similarity of a document with itself is 1.0.
self.assertTrue(numpy.alltrue(result[:i] >= 0.0))
self.assertTrue(numpy.alltrue(result[:i] < 1.0))
self.assertTrue(numpy.alltrue(result[i + 1:] >= 0.0))
self.assertTrue(numpy.alltrue(result[i + 1:] < 1.0))
def testFull(self, num_best=None):
# Override testFull.
# Single query
index = self.cls(corpus, self.similarity_matrix, num_best=num_best)
query = dictionary.doc2bow(texts[0])
sims = index[query]
if num_best is not None:
# Sparse array.
for i, sim in sims:
self.assertTrue(numpy.alltrue(sim <= 1.0))
self.assertTrue(numpy.alltrue(sim >= 0.0))
else:
self.assertAlmostEqual(1.0, sims[0]) # Similarity of a document with itself is 1.0.
self.assertTrue(numpy.alltrue(sims[1:] >= 0.0))
self.assertTrue(numpy.alltrue(sims[1:] < 1.0))
expected = 2.1889350195476758
self.assertAlmostEqual(expected, numpy.sum(sims))
# Corpora
for query in (
corpus, # Basic text corpus.
self.tfidf[corpus]): # Transformed corpus without slicing support.
index = self.cls(query, self.similarity_matrix, num_best=num_best)
sims = index[query]
if num_best is not None:
# Sparse array.
for result in sims:
for i, sim in result:
self.assertTrue(numpy.alltrue(sim <= 1.0))
self.assertTrue(numpy.alltrue(sim >= 0.0))
else:
for i, result in enumerate(sims):
self.assertAlmostEqual(1.0, result[i]) # Similarity of a document with itself is 1.0.
self.assertTrue(numpy.alltrue(result[:i] >= 0.0))
self.assertTrue(numpy.alltrue(result[:i] < 1.0))
self.assertTrue(numpy.alltrue(result[i + 1:] >= 0.0))
self.assertTrue(numpy.alltrue(result[i + 1:] < 1.0))
