induced_graph example in the documentation of Variable Elimination fails

[mroavi]

Subject of the issue

induced_graph example in https://pgmpy.org/exact_infer/ve.html throws the following error:

AttributeError: module 'networkx' has no attribute 'graph_clique_number'

Your environment

• pgmpy version: 0.1.24
• Python version: Python 3.11.6
• Operating System: Arch Linux x86_64

Steps to reproduce

Run the induced_graph example in https://pgmpy.org/exact_infer/ve.html:

import numpy as np
import pandas as pd
from pgmpy.models import BayesianNetwork
from pgmpy.inference import VariableElimination
values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
                      columns=['A', 'B', 'C', 'D', 'E'])
model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
model.fit(values)
inference = VariableElimination(model)
inference.induced_width(['C', 'D', 'A', 'B', 'E'])

Expected behaviour

Executing the last line should result in the induced width.

Actual behaviour

The following error is thrown:

AttributeError: module 'networkx' has no attribute 'graph_clique_number'

[ankurankan]

@mroavi This doesn't throw any errors for me:

In [2]: import numpy as np
   ...: import pandas as pd
   ...: from pgmpy.models import BayesianNetwork
   ...: from pgmpy.inference import VariableElimination
   ...: values = pd.DataFrame(np.random.randint(low=0, high=2, size=(1000, 5)),
   ...:                       columns=['A', 'B', 'C', 'D', 'E'])
   ...: model = BayesianNetwork([('A', 'B'), ('C', 'B'), ('C', 'D'), ('B', 'E')])
   ...: model.fit(values)
   ...: inference = VariableElimination(model)
   ...: inference.induced_width(['C', 'D', 'A', 'B', 'E'])
Out[2]: 3

Could you please check which version of networkx you are using? I am using the latest 3.2.1

[mroavi]

I also have networkx 3.2.1. Here is the rest of my installed dependencies:

pip list

Package	Version
asttokens	2.4.1
decorator	5.1.1
executing	2.0.1
filelock	3.13.1
fsspec	2023.12.2
ipython	8.21.0
jedi	0.19.1
Jinja2	3.1.3
joblib	1.3.2
MarkupSafe	2.1.4
matplotlib-inline	0.1.6
mpmath	1.3.0
networkx	3.2.1
numpy	1.26.3
nvidia-cublas-cu12	12.1.3.1
nvidia-cuda-cupti-cu12	12.1.105
nvidia-cuda-nvrtc-cu12	12.1.105
nvidia-cuda-runtime-cu12	12.1.105
nvidia-cudnn-cu12	8.9.2.26
nvidia-cufft-cu12	11.0.2.54
nvidia-curand-cu12	10.3.2.106
nvidia-cusolver-cu12	11.4.5.107
nvidia-cusparse-cu12	12.1.0.106
nvidia-nccl-cu12	2.19.3
nvidia-nvjitlink-cu12	12.3.101
nvidia-nvtx-cu12	12.1.105
opt-einsum	3.3.0
packaging	23.2
pandas	2.2.0
parso	0.8.3
patsy	0.5.6
pexpect	4.9.0
pgmpy	0.1.24
pip	23.3.2
prompt-toolkit	3.0.43
ptyprocess	0.7.0
pure-eval	0.2.2
Pygments	2.17.2
pyparsing	3.1.1
python-dateutil	2.8.2
pytz	2023.4
scikit-learn	1.4.0
scipy	1.12.0
setuptools	65.5.0
six	1.16.0
stack-data	0.6.3
statsmodels	0.14.1
sympy	1.12
threadpoolctl	3.2.0
torch	2.2.0
tqdm	4.66.1
traitlets	5.14.1
triton	2.2.0
typing_extensions	4.9.0
tzdata	2023.4
wcwidth	0.2.13

These are the steps I used to create my environment:

python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install pgmpy ipython

[ankurankan]

@mroavi Interesting. I can't think of any other reason this could be happening. Could you maybe check the networkx version in your python prompt just to make sure that the it is using the correct environment?

import networkx
networkx.__version__