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.dev202507251753365430-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.0.9.dev202507251753365430-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.dev202507251753365430-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.dev202507251753365430-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 a9c80631746197a702a257345cea7bce5e02393915f2de240989b0e23ab0062d
MD5 213b0903f84b02bb482401572754b265
BLAKE2b-256 fd4d9068b38d88e8ddcc5ba5838fc869b3a285f19896aa67fcc999fed9fa530b

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ec8402b658150ebaf53f897caa67e0b85b8eb3c01c641910f03adf14b96eacfe
MD5 65fe421fcdf805bb0a39e2fa24b78ac7
BLAKE2b-256 034b333a838558f8217cc637c88e613974244e26dc4fa06c75d22057b4f901fa

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 84495230154faf2c8765caf4cc5bd6495f9b3b26eb1020653bd0c7d2bb2c4100
MD5 52602a6f09cbbec8818162716b99f1a0
BLAKE2b-256 67689f2f9ce49456dd12e0b39df0773f82f0659024c4e0af541e0d563f5adcd0

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f2c05884ae0ee10aedc9bc4bfa0b76e051e2af7024a020d2b72373dbf989c973
MD5 045bce712bffa76662d7bf64aae3deba
BLAKE2b-256 56bd7e75977d68778a49dc62f89ef6d8a3eb6e905250fc8b44559114f645c585

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202507251753365430-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 41d8819dcbd118e1eaf23dc6905abe23b31ffb20b4aa23014edf8860fd8e6a86
MD5 ff6c3593d758b8eb34218259a2391d48
BLAKE2b-256 22db06528301f115a22705eff4e67147c3cef12ac1d3a6aeb82e89ee2cdd9f2c

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