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-2.2.1.9.dev202510031759295983-cp310-abi3-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_10_15_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 b29d92683e6551cc31dc0e1cc22fd61cb90f27bd107b94737b7aec390f941cf4
MD5 ecb3bb928e4aa13798c664ed8a6a80a2
BLAKE2b-256 431a49d890c9200a760d0db8979ea231f6b4b664ab7cc9302e7ec0aa6f2c1a3e

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 da98edcdccd1deea547d6fa84d6fa673149693036b8884fb0bf16467f9183eb8
MD5 d80b1782f6d5858b154f4127ed1b6d57
BLAKE2b-256 8d5fbc90c1c73d3a79738750933020a5823813c538cbe0d0dcaaf078193520e0

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 77098367a3f2adfb6bafe5584f1badbea1eac18e68052e7bb90eca2a328459b7
MD5 8746c84e2fa262035726379a17c4eec7
BLAKE2b-256 a6b2372c712bcf264ff65145ce5e5413baefd0b61efe047ce05fd9f62f18ab86

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 367a5f5b2116549a99169238e38fce699e0212d65c30bd09c1c1812c369218f9
MD5 4da810d1dd58856fdde23668e5c212f6
BLAKE2b-256 20c2d83928d260c3670d5c6d7aaee41c45e7aa2ba6cd508661df6d81ddc305cd

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.1.9.dev202510031759295983-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 19ed365f3c166448db86e839968fc78b1c9f98944d8fbb45811a7fc3e5728693
MD5 ba1d01c4fa78f56adb754568cefba865
BLAKE2b-256 f8971276cfc222e80e31dd370c8d43933e784e7dd7c76b91ff5a4ca0be626072

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