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-2.2.0.9.dev202508161753652324-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.2.0.9.dev202508161753652324-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-2.2.0.9.dev202508161753652324-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 38ce869b00ab18d8d57038f4a3943676d4b04e193b91a01e124655f57b4b26fa
MD5 54c898b44a112bbf2ebc410c9c71e5a1
BLAKE2b-256 08e8ab6b713bd2883fc641ff3e828815fd9c25d2c962649f123fe1834fc547b7

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 46423330dba4b4e42a625aa8a9566cbcf730838f0b638ecefd8ae185a19ff54d
MD5 3df256d8d209df3bf51209ae0fde18e5
BLAKE2b-256 190da7a04dc3c6ed18d5ac9cfac9c2c6fa8a0daf064324fc2b279e52fc67e70c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 585e3772d32e7ee06ef6ca5289c11515dffea1e1f654b0923be2b5330236526c
MD5 5e1cfecaa0ce0a0fd6e74e1596d77cb7
BLAKE2b-256 5b96f51a5b036dd174ee4bd5c21dcf89b27f3f29d89530309c8b412603650272

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7960749f5f454932b3a638600ed549bd00dac4173565894995ce41b19821a13e
MD5 29d642ea16f9a930b383bb7731cd674b
BLAKE2b-256 bb9898986b666feb556684e992bf5bac4873783add78b86ea521dd908dfd7591

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508161753652324-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 befc763375b6137b044c6d27477149e3d7bf49c3e418fc0423b33845bf373e37
MD5 b56534e9518b55d4a3dae27c59637d8a
BLAKE2b-256 05f24e58decc2581665dbdad91ff22b698675da2a9bd39d68cb482ebfd7a1f3e

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