Skip to main content

Bayesian networks and other Probabilistic Graphical Models.

Project description

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

Release history Release notifications | RSS feed

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-2.1.1.9.dev202507221750843886-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_10_15_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 4a83b9b9c7a1e8bc75de1597bf62c4c7a9d355c12750a373685d94c5f2b03ff5
MD5 e767e82cf14abb02cf28eba886a85b58
BLAKE2b-256 5e89ea5aa2d17979086aeb953c3dc074e65b1edaa8b3f9173fa2ad2edd70ccc3

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 0862f173355face5a52c1b9b8fb3c9d57140eb79ea2402a8ed3c28db57cb9f79
MD5 7b63cea19bfdd55065c1e80fc40990a4
BLAKE2b-256 46bb827df721b4836f67e0ae8d12dfbab7e0af6f37250c3bbd3b2db8db13ec34

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a95e3fed74fe7792ccdd2367b4b59144342dfe3efd4465915da8945076cf0bdd
MD5 6f2faf32e3686838034ef1d10fbd915b
BLAKE2b-256 29b26858f6396a55d581a8af5f11f6229f9bf53f5fcd1078b4c293163abea16a

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 db82f92ab2b94a99904fa1721447b0deb24e2d1bca300413265dcfd5063fc55e
MD5 40c941a24cae285721a6d2dd4e82fb91
BLAKE2b-256 0f56d75c272cbcf0d53ee307774ff569695f5400596b4779cf3aa9ea3bdbfba9

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507221750843886-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 a5c2a9e542a34104d467700cfd81053c67d87044439dfd2c52711fa918bade5f
MD5 a653e53420c05a785e911caea04248ef
BLAKE2b-256 11e98f8742d7f39c9938fb0df60649f68da8c9bef9714daa0dc9dc197f44a20a

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page