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-2.3.2.9.dev202603201770834561-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_11_0_arm64.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_10_15_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 85852d0cf3805fcc5cad60ae360e49a43286499652bb01447f49e885235b20a1
MD5 5b2cfbbf84456a933963d993990727a4
BLAKE2b-256 221d7a43b4dd7a4300ee6b7daacadf5123240e6f0d7816ae9b21b3198051ab05

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 43f2be194c8bc1ae105327d53f6d1cef6e531a19f7110cda4c5a78bb03080ea0
MD5 24c7ce7783bfe2e8b7b905e07fbc1dd5
BLAKE2b-256 b757e30a719ca9ef9460e6f41276e94a3b208ea89146bf72e4a0b7fcb45206f3

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 27a9ff82ae25e6a641d82f6a224e66505b00504ca6b26d50180c13bf6bc639fa
MD5 d14620d20b202c08928e94fac38d9d8d
BLAKE2b-256 1118a044b0e1072eb3e84a69f0bb7a0a10036e9a441dcef490caa64566f5d1bf

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7beb30efe513768b0f8ce7d6059556942465ff926988ec25fe16ffe950f76916
MD5 1f3b040f441ffe8bdb790695c23b581e
BLAKE2b-256 127ee2321e5c7553f6166556ae0eaa57b49d27e48c1165150a4ae084ab9229e8

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603201770834561-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 e9b29494e2e90af139738ca5a564b3d8c5b1a678fbdbc548526334428bb7d0b1
MD5 fa1077a66484ee07b5564aff1703af62
BLAKE2b-256 e17bed9acdc806653901313038bf13693a1944b99a21f22a7067200ce96495b4

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