Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

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

Copyright (C) 2005-2024 by Pierre-Henri WUILLEMIN et Christophe GONZALES {prenom.nom}_at_lip6.fr

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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_10_15_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 5aabd0744301c490d6c45fbf307cf835887aacb9ef07276b9a5721bcbb5b52a5
MD5 c89784200614c7a6cfa16f1b36dcf64c
BLAKE2b-256 95b3a2c3c555f7e75096cd91e76484ffcb7b957351fcc1067d349e42d79f2127

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e70e734b45a72a152db1c6a7fcb35bf7817456018689163209d59a5c9ccd5c4c
MD5 058a78a272ef62b71fc1e3ba0963a445
BLAKE2b-256 eefbc4c5ecbf34e7feab4691aa2a1785290b0aeb5e7f7e6eec54952522c790d0

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0b4012a5316810446a49a4f82ccba25ddf595453c14e4e7657ca8bc2d4ba628b
MD5 fdbc2e112ae206c176fb1e740d2a453e
BLAKE2b-256 e8cb9738f09fdcf14ef48792b06bd202f2f0f2b6968bdaa692d926f91f79f02c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 76a9347b2671b5c9214dbebb4468159e4ff446caedc06eea09df974c31ce4bcb
MD5 d304e8395b4d7073dcd9dd5a6c203b50
BLAKE2b-256 557cde4489038dbc09d23759662cad74b5efae189dddce93410c967d100f8466

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608091784631574-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 9ef4eb19fa842f2993581b8755d41e7e5c0b3895442dadd4b27b883f0e18fbdd
MD5 b9094d0e82205d48d159a1c89913cfe1
BLAKE2b-256 be0273a7d8f0d186768a5ab2cd88706ca10bd78ea4460de1bd92b6b707e55f31

See more details on using hashes here.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page