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.dev202607081781613575-cp310-abi3-win_amd64.whl (3.4 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_10_15_x86_64.whl (3.9 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 9a7efdf66566d85df8d01ab4d85a2c878abc9250b095e3e5a14b9bb4d49724e5
MD5 b442312553ca5bfb2c74b1417adfa3ea
BLAKE2b-256 2bf3bed4c7a3c6c9e6f8b4a5e8af92fc3ecbd53331a3165c6f67482347f598cd

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1fa37679e3457ef20f97eb526df6cd60bad8914c5e60c16aa9ef3b296c980663
MD5 678f8d855c28df2c15891f7fc6c2196c
BLAKE2b-256 eb909bdf33ace89ea1f8241efa4850de3347cdff007afcf42bcde75db668444a

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 0d8625452ec5df6c3878e1ad4bee9cdcadcba771b6b129df3565a72744ffe397
MD5 52ef905d925a8c484d475078dc354a38
BLAKE2b-256 f3e4a58ba9dc301051b3bb20ee303330869fa30ec860e6e09c84be16913df564

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c16642e5988afd5a73e602af698f1cb07b9ca1d8df7ea82fcdd4776ff54875cd
MD5 e4ac444ee0c7095e2432eb9a43b79cf0
BLAKE2b-256 d859e364ac72b278b4bf39bacde75c54996837387bea0a74203c7ddcf3aa0d8d

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607081781613575-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 f28ce921e491de6b7867a6784d3ab2ce1e22801a90fa7d2de100dd3fe782a67b
MD5 e167e2ed528e5b1ebfbf4cc84de808ac
BLAKE2b-256 7ef9c2d2d0e7861dd07ab622abec6dcc6860c49e7bd519931b66abc723450662

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