Comments (11)
maximtrp
commented on September 27, 2024
1
I have found the cause: random number generator was broken under Windows and MacOS (but not under Linux). The new version of package will be released shortly.
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maximtrp
commented on September 27, 2024
Hello! Thank you for reporting, but this is obviously not enough to give any feedback. Please provide the information on your corpus (total number of documents and words), model parameters and fitting process (number of iterations), and package version.
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JennieGerhardt
commented on September 27, 2024
The corpus is Chinese text split with spaces.
Model parameters :T=i, W=vocab.size, M=20, alpha=50/i, beta=0.01
100%|██████████| 18374731/18374731 [00:05<00:00, 3271476.08it/s]
100%|██████████| 20/20 [01:12<00:00, 3.64s/it]
100%|██████████| 45388/45388 [00:03<00:00, 12323.28it/s]
Version: 0.5.10
Thank you for your help!
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JennieGerhardt
commented on September 27, 2024
This is the output of test_btm.py when I run it on the SearchSnippets.txt corpus
100%|██████████| 641202/641202 [00:00<00:00, 2503526.78it/s]
100%|██████████| 20/20 [00:02<00:00, 7.64it/s]
100%|██████████| 12295/12295 [00:00<00:00, 158025.11it/s]
This is the result of p_zd /documents vs topics probability matrix.
[[9.99999967e-01 4.72269092e-09 4.72269092e-09 ... 4.72269092e-09
4.72269092e-09 4.72269092e-09]
[9.99999130e-01 1.24323721e-07 1.24323721e-07 ... 1.24323721e-07
1.24323721e-07 1.24323721e-07]
[9.99999968e-01 4.62112701e-09 4.62112701e-09 ... 4.62112701e-09
4.62112701e-09 4.62112701e-09]
...
[1.00000000e+00 2.54964359e-13 2.54964359e-13 ... 2.54964359e-13
2.54964359e-13 2.54964359e-13]
[1.00000000e+00 3.10026664e-13 3.10026664e-13 ... 3.10026664e-13
3.10026664e-13 3.10026664e-13]
[9.99999712e-01 4.11230259e-08 4.11230259e-08 ... 4.11230259e-08
4.11230259e-08 4.11230259e-08]]
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maximtrp
commented on September 27, 2024
Running 20 iterations may lead to such results. This is simply not enough for the model to converge. My recent experiments show that model perplexity stabilizes somewhere around 500 iterations.
But even with such a small number of iterations I cannot replicate this result. Could you please give the full code you are using and also pass seed value to model fit method?
from bitermplus.
JennieGerhardt
commented on September 27, 2024
class TestBTM(unittest.TestCase):
# Plotting tests
def test_btm_class(self):
with gzip_open('../dataset/SearchSnippets.txt.gz', 'rb') as file:
texts = file.readlines()
X, vocab = btm.get_words_freqs(texts)
docs_vec = btm.get_vectorized_docs(X)
biterms = btm.get_biterms(X)
LOGGER.info('Modeling started')
model = btm.BTM(X, vocab, T=8, W=vocab.size, M=20, alpha=50/8, beta=0.01)
# t1 = time.time()
model.fit(biterms, seed=12345, iterations=20)
# t2 = time.time()
# LOGGER.info(t2 - t1)
self.assertIsInstance(model.matrix_topics_words_, np.ndarray)
self.assertTupleEqual(model.matrix_topics_words_.shape, (8, vocab.size))
LOGGER.info('Modeling finished')
LOGGER.info('Inference started')
p_zd = model.transform(docs_vec)
print("sum_b",p_zd)
LOGGER.info('Inference "sum_b" finished')
p_zd = model.transform(docs_vec, infer_type='sum_w')
print("sum_w",p_zd)
LOGGER.info('Inference "sum_w" finished')
p_zd = model.transform(docs_vec, infer_type='mix')
print("mix",p_zd)
LOGGER.info('Inference "mix" finished')
LOGGER.info('Perplexity started')
perplexity = btm.perplexity(model.matrix_topics_words_, p_zd, X, 8)
self.assertIsInstance(perplexity, float)
self.assertNotEqual(perplexity, 0.)
LOGGER.info('Perplexity finished')
LOGGER.info('Coherence started')
coherence = btm.coherence(model.matrix_topics_words_, X, M=20)
self.assertIsInstance(coherence, np.ndarray)
self.assertGreater(coherence.shape[0], 0)
LOGGER.info('Coherence finished')
LOGGER.info('Model saving/loading started')
with open('model.pickle', 'wb') as file:
self.assertIsNone(pkl.dump(model, file))
with open('model.pickle', 'rb') as file:
self.assertIsInstance(pkl.load(file), btm._btm.BTM)
LOGGER.info('Model saving/loading finished')
if __name__ == '__main__':
unittest.main()I ran tests/test_btm.py directly and printed the results of p_zd for different infer_types, without making any changes to the code.
100%|██████████| 641202/641202 [00:00<00:00, 3105825.15it/s]
100%|██████████| 20/20 [00:02<00:00, 8.80it/s]
100%|██████████| 12295/12295 [00:00<00:00, 54307.48it/s]
sum_b
[[9.99989362e-01 1.51973980e-06 1.51973980e-06 ... 1.51973980e-06
1.51973980e-06 1.51973980e-06]
[9.99885135e-01 1.64092341e-05 1.64092341e-05 ... 1.64092341e-05
1.64092341e-05 1.64092341e-05]
[9.99941434e-01 8.36655753e-06 8.36655753e-06 ... 8.36655753e-06
8.36655753e-06 8.36655753e-06]
...
[9.99962688e-01 5.33031226e-06 5.33031226e-06 ... 5.33031226e-06
5.33031226e-06 5.33031226e-06]
[9.99977867e-01 3.16187601e-06 3.16187601e-06 ... 3.16187601e-06
3.16187601e-06 3.16187601e-06]
[9.99899798e-01 1.43145008e-05 1.43145008e-05 ... 1.43145008e-05
1.43145008e-05 1.43145008e-05]]sum_w
100%|██████████| 12295/12295 [00:00<00:00, 88027.49it/s]
[[9.99971071e-01 4.13271496e-06 4.13271496e-06 ... 4.13271496e-06
4.13271496e-06 4.13271496e-06]
[9.99908825e-01 1.30250357e-05 1.30250357e-05 ... 1.30250357e-05
1.30250357e-05 1.30250357e-05]
[9.99932596e-01 9.62911447e-06 9.62911447e-06 ... 9.62911447e-06
9.62911447e-06 9.62911447e-06]
...
[9.99949473e-01 7.21817072e-06 7.21817072e-06 ... 7.21817072e-06
7.21817072e-06 7.21817072e-06]
[9.99959500e-01 5.78571297e-06 5.78571297e-06 ... 5.78571297e-06
5.78571297e-06 5.78571297e-06]
[9.99910889e-01 1.27301811e-05 1.27301811e-05 ... 1.27301811e-05
1.27301811e-05 1.27301811e-05]]mix
100%|██████████| 12295/12295 [00:00<00:00, 181185.33it/s]
[[9.99999971e-01 4.08965415e-09 4.08965415e-09 ... 4.08965415e-09
4.08965415e-09 4.08965415e-09]
[9.99998995e-01 1.43548690e-07 1.43548690e-07 ... 1.43548690e-07
1.43548690e-07 1.43548690e-07]
[9.99999967e-01 4.68500846e-09 4.68500846e-09 ... 4.68500846e-09
4.68500846e-09 4.68500846e-09]
...
[1.00000000e+00 3.58952802e-13 3.58952802e-13 ... 3.58952802e-13
3.58952802e-13 3.58952802e-13]
[1.00000000e+00 3.92627489e-13 3.92627489e-13 ... 3.92627489e-13
3.92627489e-13 3.92627489e-13]
[9.99999686e-01 4.48075858e-08 4.48075858e-08 ... 4.48075858e-08
4.48075858e-08 4.48075858e-08]]from bitermplus.
maximtrp
commented on September 27, 2024
Still I cannot replicate your results. I am getting more sensible values with the same code. Please post the output of pip list and the results obtained with 200 iterations (not 20).
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JennieGerhardt
commented on September 27, 2024
Package Version
---------------------------------- ------------
-atplotlib 2.2.3
-illow 5.2.0
absl-py 0.10.0
alabaster 0.7.11
anaconda-client 1.7.2
anaconda-navigator 1.9.2
anaconda-project 0.8.2
appdirs 1.4.3
Appium-Python-Client 1.0.2
asn1crypto 0.24.0
astor 0.8.1
astroid 2.0.4
astropy 3.0.4
astunparse 1.6.3
atomicwrites 1.2.1
attrs 18.2.0
Automat 0.7.0
Babel 2.6.0
backcall 0.1.0
backports.shutil-get-terminal-size 1.0.0
beautifulsoup4 4.6.3
bert-serving 0.0.1
bert-serving-client 1.10.0
bert-serving-server 1.10.0
bert-tensorflow 1.0.4
bibtexparser 1.2.0
bitarray 0.8.3
bitermplus 0.5.10
bkcharts 0.2
blaze 0.11.3
bleach 2.1.4
bokeh 0.13.0
boto 2.49.0
boto3 1.10.48
botocore 1.13.48
Bottleneck 1.2.1
cachetools 4.1.1
certifi 2020.11.8
cffi 1.11.5
chardet 3.0.4
click 6.7
cloudpickle 0.5.5
clyent 1.2.2
cmake 3.18.4.post1
colorama 0.3.9
comtypes 1.1.7
conda 4.8.3
conda-build 3.15.1
conda-package-handling 1.7.0
constantly 15.1.0
contextlib2 0.5.5
cryptography 3.0
cssselect 1.1.0
cycler 0.10.0
Cython 0.29.14
cytoolz 0.9.0.1
dask 0.19.1
datashape 0.5.4
decorator 4.3.0
defusedxml 0.5.0
distributed 1.23.1
docutils 0.14
docx 0.2.4
emoji 0.6.0
entrypoints 0.2.3
et-xmlfile 1.0.1
fake-useragent 0.1.11
Faker 4.1.1
fastcache 1.0.2
filelock 3.0.8
Flask 1.0.2
Flask-Cors 3.0.6
funcy 1.15
future 0.18.2
gast 0.3.3
gensim 3.8.3
gevent 1.3.6
glob2 0.6
google-api-core 1.23.0
google-auth 1.23.0
google-auth-oauthlib 0.4.1
google-cloud-language 2.0.0
google-pasta 0.2.0
googleapis-common-protos 1.52.0
GPUtil 1.4.0
greenlet 0.4.15
grpcio 1.33.2
h5py 2.10.0
heapdict 1.0.0
html5lib 1.0.1
hyperlink 18.0.0
idna 2.10
imageio 2.4.1
imagesize 1.1.0
importlib-metadata 1.7.0
incremental 17.5.0
ipykernel 4.10.0
ipython 6.5.0
ipython-genutils 0.2.0
ipywidgets 7.4.1
isort 4.3.4
itsdangerous 0.24
jdcal 1.4
jedi 0.12.1
jieba 0.42.1
Jinja2 2.10
jmespath 0.10.0
joblib 0.16.0
jsonschema 2.6.0
jupyter 1.0.0
jupyter-client 5.2.3
jupyter-console 5.2.0
jupyter-core 4.4.0
jupyterlab 0.34.9
jupyterlab-launcher 0.13.1
Keras 2.2.4
Keras-Applications 1.0.8
keras-bert 0.82.0
keras-embed-sim 0.8.0
keras-layer-normalization 0.14.0
keras-multi-head 0.27.0
keras-pos-embd 0.11.0
keras-position-wise-feed-forward 0.6.0
Keras-Preprocessing 1.1.2
keras-self-attention 0.46.0
keras-transformer 0.38.0
keyring 13.2.1
kiwisolver 1.0.1
lazy-object-proxy 1.3.1
libcst 0.3.13
llvmlite 0.24.0
locket 0.2.0
lxml 4.2.5
Markdown 3.2.2
MarkupSafe 1.0
matplotlib 3.3.2
mccabe 0.6.1
menuinst 1.4.14
mglearn 0.1.9
mistune 0.8.3
mkl-fft 1.0.4
mkl-random 1.0.1
mmdnn 0.1.3
mock 4.0.2
more-itertools 4.3.0
mouse 0.7.1
move 0.1.3
mpmath 1.0.0
msgpack 0.5.6
MulticoreTSNE 0.1
multipledispatch 0.6.0
mypy-extensions 0.4.3
mysqlclient 2.0.1
navigator-updater 0.2.1
nbconvert 5.4.0
nbformat 4.4.0
networkx 2.1
nltk 3.5
nose 1.3.7
notebook 5.6.0
numba 0.39.0
numexpr 2.6.8
numpy 1.18.5
numpydoc 0.8.0
oauthlib 3.1.0
odo 0.5.1
olefile 0.46
openpyxl 2.5.6
opt-einsum 3.3.0
packaging 17.1
pandas 0.23.4
pandocfilters 1.4.2
parsel 1.5.2
parso 0.3.1
partd 0.3.8
path.py 11.1.0
pathlib2 2.3.2
patsy 0.5.0
pep8 1.7.1
pickleshare 0.7.4
Pillow 7.2.0
pip 20.2.2
pkginfo 1.4.2
pluggy 0.7.1
ply 3.11
prometheus-client 0.3.1
prompt-toolkit 1.0.15
proto-plus 1.11.0
protobuf 3.13.0
psutil 5.4.7
py 1.6.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycodestyle 2.4.0
pycosat 0.6.3
pycparser 2.18
pycrypto 2.6.1
pycurl 7.43.0.5
PyDispatcher 2.0.5
pyflakes 2.0.0
Pygments 2.2.0
PyHamcrest 2.0.2
pyLDAvis 2.1.2
pylint 2.1.1
pymongo 3.11.0
PyMouse 1.0
PyMySQL 0.10.0
pyodbc 4.0.24
pyOpenSSL 19.1.0
pyparsing 2.2.0
PySocks 1.6.8
pytest 3.8.0
pytest-arraydiff 0.2
pytest-astropy 0.4.0
pytest-doctestplus 0.1.3
pytest-openfiles 0.3.0
pytest-remotedata 0.3.0
pytest-runner 5.2
python-dateutil 2.7.3
python-docx 0.8.10
pytorch-pretrained-bert 0.6.2
pytz 2020.4
PyWavelets 1.0.0
pywin32 223
pywinpty 0.5.4
PyYAML 5.3.1
pyzmq 17.1.2
qt5reactor 0.6.3
QtAwesome 0.4.4
qtconsole 4.4.1
QtPy 1.5.0
queuelib 1.5.0
redis 3.5.3
regex 2020.7.14
requests 2.25.0
requests-oauthlib 1.3.0
rope 0.11.0
rsa 4.6
ruamel-yaml 0.15.46
s3transfer 0.2.1
scikit-image 0.14.0
scikit-learn 0.19.2
scipy 1.4.1
Scrapy 1.6.0
seaborn 0.9.0
selenium 3.141.0
Send2Trash 1.5.0
service-identity 17.0.0
setuptools 50.3.2
simplegeneric 0.8.1
singledispatch 3.4.0.3
six 1.15.0
smart-open 2.1.1
snowballstemmer 1.2.1
snownlp 0.12.3
sortedcollections 1.0.1
sortedcontainers 2.0.5
Sphinx 1.7.9
sphinxcontrib-websupport 1.1.0
spyder 3.3.1
spyder-kernels 0.2.6
SQLAlchemy 1.2.11
statsmodels 0.9.0
stop-words 2018.7.23
sympy 1.1.1
tables 3.4.4
tblib 1.3.2
tensorboard 1.14.0
tensorboard-plugin-wit 1.7.0
tensorflow-estimator 1.14.0
tensorflow-gpu 1.14.0
termcolor 1.1.0
terminado 0.8.1
testpath 0.3.1
text-unidecode 1.3
toolz 0.9.0
torch 1.6.0+cu101
torchvision 0.7.0+cu101
tornado 5.1
tqdm 4.26.0
traitlets 4.3.2
Twisted 18.7.0
typing-extensions 3.7.4.3
typing-inspect 0.6.0
unicodecsv 0.14.1
urllib3 1.25.10
w3lib 1.21.0
wcwidth 0.1.7
webencodings 0.5.1
Werkzeug 0.14.1
wheel 0.31.1
widgetsnbextension 3.4.1
win-inet-pton 1.0.1
win-unicode-console 0.5
wincertstore 0.2
wrapt 1.12.1
xlrd 1.1.0
XlsxWriter 1.1.0
xlwings 0.11.8
xlwt 1.3.0
zict 0.1.3
zipp 3.1.0
zope.interface 4.5.0
Iterations = 500 gives the same result
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maximtrp
commented on September 27, 2024
I have managed to reproduce this bug under MacOS and Windows, but model is being fitted correctly under Linux. I will try to figure out the cause
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JennieGerhardt
commented on September 27, 2024
My OS is Win10.
I created a new virtual environment and updated the packages, but it still doesn't work.
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maximtrp
commented on September 27, 2024
Thank you again for drawing attention to this problem! It is now fixed, and the new release is available in PyPi.
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Related Issues (20)
- Is it possible to contain only those words that occur in max 90% and min 10% of documents in function X, vocabulary, vocab_dict = btm.get_words_freqs() HOT 1
- Cannot find Closest topics and Stable topics HOT 4
- How to give each feature a weight value?
- Questions regarding Perplexity and Model Comparison with C++ HOT 3
- get_top_topic_words yields unreasonable results HOT 1
- Implementation Guide HOT 2
- Getting the error 'CountVectorizer' object has no attribute 'get_feature_names_out' HOT 4
- The vocabularies input into BTM HOT 1
- Got an unexpected result in marked sample HOT 7
- Calculation of nmi,ami,ri HOT 2
- Failed building wheel for bitermplus HOT 2
- failed building wheels HOT 2
- ERROR: Failed building wheel for bitermplus HOT 9
- Calculating wrong perplexity? HOT 2
- Using `biterm.perplexity()` for Calculating Perplexity of Other Topic Models HOT 1
- Visualization poblem HOT 7
- Installation errors with Mac OS HOT 2
- Topics' names? HOT 3
- Linux Installation of pythonx.x-dev needed if installing in a virtual environment HOT 1
- ValueError: too many values to unpack (expected 3) HOT 1
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