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-2.3.1.9.dev202601011765915415-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-macosx_11_0_arm64.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.1.9.dev202601011765915415-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-2.3.1.9.dev202601011765915415-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 580b86f9d9fc1b96217c857e4f7405615c4146e6e59899e2cda0d6eec56ffae1
MD5 f9b7893ebd0fac7811a9582b7dce7a1e
BLAKE2b-256 50a900e7f3b2102cf2864526c632e743713fcbfe0fc57ab782b3aebff0228f45

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3175b1bacff017012ba5a9aefb035ef90b7a7626cb70f10930a2ef731f4bad93
MD5 b2235aa7f006ec41ccf4e3253617969d
BLAKE2b-256 0b929b52b624b5f2c1d18cb91e482e0d26b4d52539b4d7b4d55dd20e1c4e989e

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 7bf176de79aa4908080409acc36fd5c4dc8e6f800dd0ca1a2e113905ac336ef2
MD5 8e9ad6bbadc61d4d3129e3f0aebaaf02
BLAKE2b-256 c518cd4389c5b21f7fdc67e2e092fce04fd950600c6b0002e0738a48e80c1ba8

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 197dc5504f5cfc3aa6bd09dca883626b99886964325da64d559d182328d0bbeb
MD5 64c583eee4875ff6c9a235f8a1736075
BLAKE2b-256 6c168fa75d7912de9bde2d1970b64067e0132f2bcae5974512064faf8cdb5068

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601011765915415-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 fed6cdc3ab622efc43296dc85063227fac4c1c07bbba7664a0a87bfba176d5e7
MD5 069d88897f26d66d3eb26f9b7b72cd7f
BLAKE2b-256 f5f2cbff0df8f8a98677c49a50762a979d5f92c31eebf0d26aa5da1062d61309

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