pyAgrum

pyAgrum is a scientific C++ and Python library dedicated to Bayesian Networks and other Probabilistic Graphical Models. It provides a high-level interface to the part of aGrUM allowing to create, model, learn, use, calculate with and embed Bayesian Networks and other graphical models. Some specific (python and C++) codes are added in order to simplify and extend the aGrUM API.

Important

Since pyAgrum 2.0.0, the package name follows PEP8 rules and is now pyagrum (lowercase). Please use import pyagrum instead of import pyAgrum in your code.

See the CHANGELOG for more details.

Example

import pyagrum as gum

# Creating BayesNet with 4 variables
bn=gum.BayesNet('WaterSprinkler')
print(bn)

# Adding nodes the long way
c=bn.add(gum.LabelizedVariable('c','cloudy ?',["Yes","No"]))
print(c)

# Adding nodes the short way
s, r, w = [ bn.add(name, 2) for name in "srw" ]
print (s,r,w)
print (bn)

# Addings arcs c -> s, c -> r, s -> w, r -> w
bn.addArc(c,s)
for link in [(c,r),(s,w),(r,w)]:
bn.addArc(*link)
print(bn)

# or, equivalenlty, creating the BN with 4 variables, and the arcs in one line
bn=gum.fastBN("w<-r<-c{Yes|No}->s->w")

# Filling CPTs
bn.cpt("c").fillWith([0.5,0.5])
bn.cpt("s")[0,:]=0.5 # equivalent to [0.5,0.5]
bn.cpt("s")[{"c":1}]=[0.9,0.1]
bn.cpt("w")[0,0,:] = [1, 0] # r=0,s=0
bn.cpt("w")[0,1,:] = [0.1, 0.9] # r=0,s=1
bn.cpt("w")[{"r":1,"s":0}] = [0.1, 0.9] # r=1,s=0
bn.cpt("w")[1,1,:] = [0.01, 0.99] # r=1,s=1
bn.cpt("r")[{"c":0}]=[0.8,0.2]
bn.cpt("r")[{"c":1}]=[0.2,0.8]

# Saving BN as a BIF file
gum.saveBN(bn,"WaterSprinkler.bif")

# Loading BN from a BIF file
bn2=gum.loadBN("WaterSprinkler.bif")

# Inference
ie=gum.LazyPropagation(bn)
ie.makeInference()
print (ie.posterior("w"))

# Adding hard evidence
ie.setEvidence({"s": 1, "c": 0})
ie.makeInference()
print(ie.posterior("w"))

# Adding soft and hard evidence
ie.setEvidence({"s": [0.5, 1], "c": 0})
ie.makeInference()
print(ie.posterior("w"))

LICENSE

The aGrUM/pyAgrum library and all its derivatives are distributed under the dual LGPLv3+MIT license, see LICENSE.LGPL and LICENSE.MIT.

You can therefore integrate this library into your software solution but it will remain covered by either the LGPL v.3 license or the MIT license or, as aGrUM itself, by the dual LGPLv3+MIT license at your convenience. If you wish to integrate the aGrUM library into your product without being affected by this license, please contact us (info@agrum.org).

This library depends on different third-party codes. See src/aGrUM/tools/externals for specific COPYING and explicit permission of the authors, if needed.

If you use aGrUM/pyAgrum as a dependency of your own project, you are not contaminated by the GPL license of some of these third-party codes as long as you use only their aGrUM/pyAgrum interfaces and not their native interfaces.

Authors

Pierre-Henri Wuillemin
Christophe Gonzales

Maintainers

Lionel Torti
Gaspard Ducamp

Metadata

Release files for pyAgrum 3.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for pyAgrum 3.2.1
File	Interpreter	ABI	Platform	Reset
pyagrum-3.2.1-cp310-abi3-win_amd64.whl 6.6 MB Oct 9, 2026	CPython 3.10	abi3	Windows x86-64	Details
pyagrum-3.2.1-cp310-abi3-manylinux2014_x86_64.whl 7.9 MB Oct 9, 2026	CPython 3.10	abi3	Linux glibc 2.17+ x86-64	Details
pyagrum-3.2.1-cp310-abi3-manylinux2014_aarch64.whl 7.1 MB Oct 9, 2026	CPython 3.10	abi3	Linux glibc 2.17+ ARM64	Details
pyagrum-3.2.1-cp310-abi3-macosx_11_0_arm64.whl 5.2 MB Oct 9, 2026	CPython 3.10	abi3	macOS 11.0+ ARM64	Details
pyagrum-3.2.1-cp310-abi3-macosx_10_15_x86_64.whl 5.9 MB Oct 9, 2026	CPython 3.10	abi3	macOS 10.15+ x86-64	Details

Total release size: 32.6 MB

Release files / pyagrum-3.2.1-cp310-abi3-win_amd64.whl

Download URL	pyagrum-3.2.1-cp310-abi3-win_amd64.whl
Size	6.6 MB
Tags	CPython 3.10 Windows x86-64 abi3
SHA-256 checksum How to use checksums	`af76fda16a63559555558d80880fbfe2db2955d475c1a5440e1b52386fc8a63b`
BLAKE2b-256 checksum How to use checksums	`95f521d9edb7bf901457d66031c1c11edd9b1a295d6cdd432e74ccb5b9dcf874`
Upload date	Oct 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.8.10`

Release files / pyagrum-3.2.1-cp310-abi3-manylinux2014_x86_64.whl

Download URL	pyagrum-3.2.1-cp310-abi3-manylinux2014_x86_64.whl
Size	7.9 MB
Tags	CPython 3.10 Linux glibc 2.17+ x86-64 abi3
SHA-256 checksum How to use checksums	`f89ce7a2f43a9ca3824aad8dadfb9287458bc28ed0aff49cb660a276259cf603`
BLAKE2b-256 checksum How to use checksums	`6aa5f7f82aa9235096da4e9cd0cb1736858e59e7846ec1eb9d6f746084e32ccf`
Upload date	Oct 9, 2026
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Uploaded via	`twine/6.1.0 CPython/3.8.10`

Release files / pyagrum-3.2.1-cp310-abi3-manylinux2014_aarch64.whl

Download URL	pyagrum-3.2.1-cp310-abi3-manylinux2014_aarch64.whl
Size	7.1 MB
Tags	CPython 3.10 Linux glibc 2.17+ ARM64 abi3
SHA-256 checksum How to use checksums	`14a105647cace9900774788b4391dcf62a595fd38bf98e5e5b0ebd4de647f477`
BLAKE2b-256 checksum How to use checksums	`f16dba21802457662cc940332a85bc42a10d1bfb246e459c9744a663502fd71c`
Upload date	Oct 9, 2026
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Uploaded via	`twine/6.1.0 CPython/3.8.10`

Release files / pyagrum-3.2.1-cp310-abi3-macosx_11_0_arm64.whl

Download URL	pyagrum-3.2.1-cp310-abi3-macosx_11_0_arm64.whl
Size	5.2 MB
Tags	CPython 3.10 abi3 macOS 11.0+ ARM64
SHA-256 checksum How to use checksums	`2929d8c66150005113ee923af02c65c13524f14f2654805e1d0387270ace1842`
BLAKE2b-256 checksum How to use checksums	`8a922710313d51548ffd694f8e1fdfb8107c2cae9e2368981e6e39f80683eb5f`
Upload date	Oct 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.8.10`

Release files / pyagrum-3.2.1-cp310-abi3-macosx_10_15_x86_64.whl

Download URL	pyagrum-3.2.1-cp310-abi3-macosx_10_15_x86_64.whl
Size	5.9 MB
Tags	CPython 3.10 abi3 macOS 10.15+ x86-64
SHA-256 checksum How to use checksums	`9c7d8dbaf62abc4ac6ae6bf84e4a131c2daa435dddced2e2ee2f67b54fb5ce44`
BLAKE2b-256 checksum How to use checksums	`3c3e9948acb2b20fe108bd3021e4b9ba0d6a58736a9bb8bbec5aef1a270c2bd7`
Upload date	Oct 9, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.8.10`