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.dev202508091753652324-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.0.9.dev202508091753652324-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.dev202508091753652324-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.dev202508091753652324-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 b3475ae230e57a806508f8e865a228d9cdedeade68425da4024194905a3beb94
MD5 5a026691c673cb31838a7dc397d8d348
BLAKE2b-256 0da35dcb4e9aa5c325a7dc30796984f4fe8f4cc5b0d8fe6fa6cb3e56bc04d709

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e2af1931c054cbc3c9d86b031e5d0600494824c0852128bf824d15cc6ad60522
MD5 c1f14abc3a4b5de3fec2fbec0637e924
BLAKE2b-256 4b07b8c9a2ed323e292e3d21c4a5f08f57ea908fb25059728ffd9a497325a1ef

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ecc50bc49f9667ce344bb3d61487ad007a78488b8312ad088f7f63baf2fbf07f
MD5 aaa66b904d6dc42bbf193fdb5e36b1d6
BLAKE2b-256 bf1e32a3bf7b4208b26581f61b6c4f2c98983709661e156cb24d1c264c5fd603

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0417d866f4259d88dfd862e09052789af7624d0fca6a0fa136a11365e996dd34
MD5 0601d3fbf3477e3bd7a0a5031fa00127
BLAKE2b-256 a3d353266d7194f586e2a6f071dc8c4a3dd42db2d52e4ba097479b4d0b965971

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508091753652324-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 2284a83071beee38348cb155f617ce76286c4f98c451e20b66259c2ad3caaace
MD5 9dbdcad31003b4a410a77a42fa8f0810
BLAKE2b-256 ac9cf8a16105614b2d1d180f0087d7301561c001b52fbc3db8fccdb58fbc6b48

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