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.dev202507051750843886-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.1.1.9.dev202507051750843886-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.dev202507051750843886-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.dev202507051750843886-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 b050afb292b6dd1521b55562412d63f412f0af75347083146049d56943ff7b32
MD5 da606db6775e0644507d694c14ce7d1a
BLAKE2b-256 c7c9ebf4f539a78072ac5d5b601361d7c3bc20071c441a8d37b53c6aa13cc01f

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 aee6a66c49d004732626d729a61dc0cfe778486cdea32f3ce2446bf5feae6fca
MD5 2c72785f465c9cb89e31990b045e9543
BLAKE2b-256 aad9bc4667a254b8168d9c54295a6dc38c4976d7a2c3331bdaa80e40354bc638

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 dfbc957c85d7263bca9aa27991b296cb41133d1bffb528f41cb107230c434f13
MD5 22950a4dd59d6fc3c8a29c3ccdc8013c
BLAKE2b-256 4746e22dd375e2280b6ab61d89c73027ef42b3373404e8f14ed79d35aa5cd76c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 e3c057a6e6e74e54a85db544c64aea109edcee270918512418e15fe76b343661
MD5 ef3365dbdf151eb869dbf51d03d32ffe
BLAKE2b-256 e74a99ba693ebc6d43fd7880e2f34562ab8db4ff1d3c219890d35b790b5313fc

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507051750843886-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 03f00cadb27994c3842ed396cbd84d77c20dd7a8b057ccfa2ff43f35c210388e
MD5 6770d050ffe1fe786ce1dea5a8d6349e
BLAKE2b-256 49578102a1a79903cffb9aa851637ac4d87831f452fa54bbf577cab9d45db2b3

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