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License: MIT License
Bayesian Networks in Python
License: MIT License
Currently doesn't work.
Drawing/Plotting is having some issues due to graphviz/dot engine..
Make sure marginal inference works after moving the main drivers away from the respective classes and into the inference folder.
I would like to try your code. Do you have an installer that works with newer versions of Python? Currently, I cannot get the code to execute. Ideally, setup.py should be run with
pip install setup.py
but the code does not appear to work in that way. Any help is appreciated. Thanks.
Hi,
I am trying to use this package, but I cannot install it on my system. I could download and unziped the pyBN-master correctly. Then I opened my anaconda prompt and changed the directory to cd /d D:......\pyBN-master. After that I typed python and then typed "from pyBN import" as mentioned in the "Usage" section, but I faced the SyntaxError: invalid syntax
It would be appreciated if anyone could help me.
Best,
test and rewrite factor code
mle and bayesian methods
delete/add as necessary
Encountered this problem on pyBN/pyBN/classes/factor.py
, method reduce_factor
` 585 """
586 exp_len = len(self.cpt)/float(self.card[rv])
--> 587 new_cpt = np.zeros((exp_len,))
588
589 val_idx = self.bn.F[rv]['values'].index(val)
TypeError: 'float' object cannot be interpreted as an index`
In line 587, the exp_len will be type float and the newer version of numpy does not accept it.
From numpy release note:
DeprecationWarning to error
Indexing with floats raises IndexError, e.g., a[0, 0.0].
Indexing with non-integer array_like raises IndexError, e.g., a['1', '2']
Indexing with multiple ellipsis raises IndexError, e.g., a[..., ...].
Non-integers used as index values raise TypeError, e.g., in reshape, take, and specifying reduce axis.
I reinstall the numpy==1.11.3 and that fixed the problem. But I suggest that the type should be cast to int, and there might be other places that has the same problem, too.
Write code for edge orientation after structure learning - for instance, PC algorithm only gives an undirected graph right now. Perhaps make "orient=True" as an argument so users don't have to orient them (ala bnlearn)
The algorithm in topsort() is slightly different from the one in wiki. When there is a triangle like A->B->C and A->C, the function could return a wrong result [A, C, B], depending on the order of the input edge_dict.
populate those and make decisions on their design/use
Remove bn.data structure and replace it with a numpy list of "Factor" objects.. they both hold the same information but the Factor object is much faster..
get networkx out of the package. Currently mostly used for topological sort - write my own in numba
there is a erroe: ModuleNotFoundError: No module named 'pyBN.structure_learn'
I want using another scoring function to learn the structure, I wonder if there is a example for structure_learn using bic, bde or LL.
thanks a lot.
In pyBN.learning.structure.tree.chow_liu
for i,j,w in edge_list:
if i in vertex_cache and j not in vertex_cache:
mst[i].append(j)
vertex_cache.add(j)
elif i not in vertex_cache and j in vertex_cache:
mst[j].append(i)
vertex_cache.add(i)
This code is wrong. Since once an edge i->j both not in vertex_cache, It will not be considerred any longer. Even later, when one of them, say i, is added to vertex_cache, apparently i->j would be a safe link, but won't be considerred, leading to worse spanning tree.
If you are implementing the Kruskal's algorithm, then you should consider the isolated components in current forest, and include the edge when FIND-SET(i) ~= FIND-SET(j).
If you are implementing the Prim's algorithm, then you probably should use a while loop. And inside it, use priority queue (max heap) to add max weight node into the mst.
make sure fileio works.
check algorithm
I created BN from a BIF file (asia.bif) . But when I try to find the score using BDe with 'lizards.csv' it is failing.
Code -
from pyBN import *
import numpy as np
import os
from os.path import dirname
file = 'data/asia.bif'
bn = read_bn(file)
dpath = os.path.join(dirname(dirname(dirname(dirname(file)))),'data')
path = (os.path.join(dpath,'lizards.csv'))
data = np.loadtxt(path, dtype='int32',skiprows=1,delimiter=',')
print BDe(bn,data)
Traceback (most recent call last):
File "test.py", line 12, in
print BDe(bn,data)
File "/home/sonu/Documents/pyBN/pyBN-master/pyBN/learning/structure/score/bayes_scores.py", line 61, in BDe
counts_dict = mle_fast(bn, data, counts=True, np=True)
File "/home/sonu/Documents/pyBN/pyBN-master/pyBN/learning/parameter/mle.py", line 41, in mle_fast
F[n]['values'] = list(nmp.unique(data[:,i]))
IndexError: index 3 is out of bounds for axis 1 with size 3
Loopy Belief Propagation and Message Passing don't currently work.
In the hill_climbing function, I have to insert ''data''. How is made this array?
Code to merge BNs based on parameters (assuming identical structure).. and maybe structure eventually.
There is a note in the MLE estimator code that says:
Notes
-----
- Currently doesn't return correct solution
Is this still true? can it return the correct solution?
Functionality for drawing BNs -- perhaps using networkx?
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