def test_generate_wordclouds_for_topic_words():
try:
import lda
import PIL
from wordcloud import WordCloud
except ImportError:
pytest.skip('at least one of lda, Pillow, wordcloud not installed')
data = model_io.load_ldamodel_from_pickle('tests/data/tiny_model_reuters_5_topics.pickle')
model = data['model']
vocab = data['vocab']
phi = model.topic_word_
assert phi.shape == (5, len(vocab))
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(phi, vocab, 10)
assert len(topic_word_clouds) == 5
assert set(topic_word_clouds.keys()) == set('topic_%d' % i for i in range(1, 6))
assert all(isinstance(wc, PIL.Image.Image) for wc in topic_word_clouds.values())
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(phi, vocab, 10,
which_topics=('topic_1', 'topic_2'),
return_images=False,
width=640, height=480)
assert set(topic_word_clouds.keys()) == {'topic_1', 'topic_2'}
assert all(isinstance(wc, WordCloud) for wc in topic_word_clouds.values())
assert all(wc.width == 640 and wc.height == 480 for wc in topic_word_clouds.values())
def test_generate_wordclouds_for_document_topics():
try:
import lda
import PIL
from wordcloud import WordCloud
except ImportError:
pytest.skip('at least one of lda, Pillow, wordcloud not installed')
data = model_io.load_ldamodel_from_pickle('tests/data/tiny_model_reuters_5_topics.pickle')
model = data['model']
doc_labels = data['doc_labels']
theta = model.doc_topic_
assert theta.shape == (len(doc_labels), 5)
doc_topic_clouds = visualize.generate_wordclouds_for_document_topics(theta, doc_labels, 3)
assert len(doc_topic_clouds) == len(doc_labels)
assert set(doc_topic_clouds.keys()) == set(doc_labels)
assert all(isinstance(wc, PIL.Image.Image) for wc in doc_topic_clouds.values())
which_docs = doc_labels[:2]
assert len(which_docs) == 2
doc_topic_clouds = visualize.generate_wordclouds_for_document_topics(theta, doc_labels, 3,
which_documents=which_docs,
return_images=False,
width=640, height=480)
assert set(doc_topic_clouds.keys()) == set(which_docs)
assert all(isinstance(wc, WordCloud) for wc in doc_topic_clouds.values())
assert all(wc.width == 640 and wc.height == 480 for wc in doc_topic_clouds.values())
def test_exclude_topics(exclude, pass_topic_word, renormalize, return_new_topic_mapping):
try:
import lda
except ImportError:
pytest.skip('lda not installed')
data = model_io.load_ldamodel_from_pickle('tests/data/tiny_model_reuters_5_topics.pickle')
model = data['model']
exclude_ind = list(set(exclude))
n_exclude = len(exclude_ind)
res = model_stats.exclude_topics(exclude_ind,
model.doc_topic_,
model.topic_word_ if pass_topic_word else None,
renormalize=renormalize,
return_new_topic_mapping=return_new_topic_mapping)
if pass_topic_word and return_new_topic_mapping:
assert isinstance(res, tuple)
assert len(res) == 3
new_theta, new_phi, topic_mapping = res
elif pass_topic_word and not return_new_topic_mapping:
assert isinstance(res, tuple)
assert len(res) == 2
new_theta, new_phi = res
elif not pass_topic_word and return_new_topic_mapping:
assert isinstance(res, tuple)
assert len(res) == 2
new_theta, topic_mapping = res
else: # not pass_topic_word and not return_new_topic_mapping:
assert not isinstance(res, tuple)
new_theta = res
assert new_theta.shape == (model.doc_topic_.shape[0], model.doc_topic_.shape[1] - n_exclude)
if pass_topic_word:
assert new_phi.shape == (model.topic_word_.shape[0] - n_exclude, model.topic_word_.shape[1])
if new_theta.shape[1] > 0:
if renormalize:
assert np.allclose(np.sum(new_theta, axis=1), 1)
else:
assert np.all(np.sum(new_theta, axis=1) <= 1 + 1e-5)
if return_new_topic_mapping:
old_indices = list(topic_mapping.keys())
new_indices = list(topic_mapping.values())
assert len(old_indices) == len(new_indices) == new_theta.shape[1]
assert 0 <= min(old_indices) < model.doc_topic_.shape[1]
assert 0 <= max(old_indices) < model.doc_topic_.shape[1]
assert 0 <= min(new_indices) < new_theta.shape[1]
assert 0 <= max(new_indices) < new_theta.shape[1]
for old_ind, new_ind in topic_mapping.items():
old_t = model.doc_topic_[:, old_ind]
new_t = new_theta[:, new_ind]
assert np.allclose(old_t, new_t)
if pass_topic_word:
assert np.allclose(model.topic_word_[old_ind, :], new_phi[new_ind, :])
def test_generate_wordclouds_for_document_topics():
py3file = '.py3' if six.PY3 else ''
data = model_io.load_ldamodel_from_pickle(
'tests/data/tiny_model_reuters_5_topics%s.pickle' % py3file)
model = data['model']
doc_labels = data['doc_labels']
theta = model.doc_topic_
assert theta.shape == (len(doc_labels), 5)
doc_topic_clouds = visualize.generate_wordclouds_for_document_topics(
theta, doc_labels, 3)
assert len(doc_topic_clouds) == len(doc_labels)
assert set(doc_topic_clouds.keys()) == set(doc_labels)
assert all(
isinstance(wc, PIL.Image.Image)
for wc in doc_topic_clouds.values())
which_docs = doc_labels[:2]
assert len(which_docs) == 2
doc_topic_clouds = visualize.generate_wordclouds_for_document_topics(
theta,
doc_labels,
3,
which_documents=which_docs,
return_images=False,
width=640,
height=480)
assert set(doc_topic_clouds.keys()) == set(which_docs)
assert all(
isinstance(wc, WordCloud) for wc in doc_topic_clouds.values())
assert all(wc.width == 640 and wc.height == 480
for wc in doc_topic_clouds.values())
def test_generate_wordclouds_for_topic_words():
py3file = '.py3' if six.PY3 else ''
data = model_io.load_ldamodel_from_pickle(
'tests/data/tiny_model_reuters_5_topics%s.pickle' % py3file)
model = data['model']
vocab = data['vocab']
phi = model.topic_word_
assert phi.shape == (5, len(vocab))
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(
phi, vocab, 10)
assert len(topic_word_clouds) == 5
assert set(topic_word_clouds.keys()) == set('topic_%d' % i
for i in range(1, 6))
assert all(
isinstance(wc, PIL.Image.Image)
for wc in topic_word_clouds.values())
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(
phi,
vocab,
10,
which_topics=('topic_1', 'topic_2'),
return_images=False,
width=640,
height=480)
assert set(topic_word_clouds.keys()) == {'topic_1', 'topic_2'}
assert all(
isinstance(wc, WordCloud) for wc in topic_word_clouds.values())
assert all(wc.width == 640 and wc.height == 480
for wc in topic_word_clouds.values())
def test_save_load_ldamodel_pickle():
pfile = 'tests/data/test_pickle_unpickle_ldamodel.pickle'
dtm = np.array([[0, 1], [2, 3], [4, 5], [6, 0]])
doc_labels = ['doc_' + str(i) for i in range(dtm.shape[0])]
vocab = ['word_' + str(i) for i in range(dtm.shape[1])]
model = lda.LDA(2, n_iter=1)
model.fit(dtm)
model_io.save_ldamodel_to_pickle(pfile, model, vocab, doc_labels)
unpickled = model_io.load_ldamodel_from_pickle(pfile)
assert np.array_equal(model.doc_topic_, unpickled['model'].doc_topic_)
assert np.array_equal(model.topic_word_, unpickled['model'].topic_word_)
assert vocab == unpickled['vocab']
assert doc_labels == unpickled['doc_labels']
def test_write_wordclouds_to_folder(tmpdir):
path = tmpdir.mkdir('wordclouds').dirname
py3file = '.py3' if six.PY3 else ''
data = model_io.load_ldamodel_from_pickle(
'tests/data/tiny_model_reuters_5_topics%s.pickle' % py3file)
model = data['model']
vocab = data['vocab']
phi = model.topic_word_
assert phi.shape == (5, len(vocab))
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(
phi, vocab, 10)
visualize.write_wordclouds_to_folder(topic_word_clouds, path,
'cloud_{label}.png')
for label in topic_word_clouds.keys():
assert os.path.exists(
os.path.join(path, 'cloud_{label}.png'.format(label=label)))
def test_write_wordclouds_to_folder(tmpdir):
try:
import lda
import PIL
from wordcloud import WordCloud
except ImportError:
pytest.skip('at least one of lda, Pillow, wordcloud not installed')
path = tmpdir.mkdir('wordclouds').dirname
data = model_io.load_ldamodel_from_pickle('tests/data/tiny_model_reuters_5_topics.pickle')
model = data['model']
vocab = data['vocab']
phi = model.topic_word_
assert phi.shape == (5, len(vocab))
topic_word_clouds = visualize.generate_wordclouds_for_topic_words(phi, vocab, 10)
visualize.write_wordclouds_to_folder(topic_word_clouds, path, 'cloud_{label}.png')
for label in topic_word_clouds.keys():
assert os.path.exists(os.path.join(path, 'cloud_{label}.png'.format(label=label)))
