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.dev202604111770834561-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.2.9.dev202604111770834561-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-2.3.2.9.dev202604111770834561-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 490fe39e96e4cf0b432e505a27b000d1cb89b4807797ca3217fc8ca6d707edff
MD5 d814488ef57b90fac6530822017515c5
BLAKE2b-256 f8c9e7906a854a961d0026d84589ff0ec2da50a365a37bd323b4ae17728fc816

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d3be8b11d39da57333edf8db19a9d9663e0b418099863c2e982db3ac1367f3b6
MD5 5f628dbad945586b3377cb2a7f0aa040
BLAKE2b-256 06bb323571913cf1d93f968bf7091f767e9010b0884e9c8f48e838bd7b4ca2f9

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 91d4b9a6399052384e7c85433be5618d6c97d1eb3c30fb6924713ddc43cb0845
MD5 58b776e5f944f05c1a9c03d2305cdc70
BLAKE2b-256 6ed049cb1be29029011c5ddd62a45a4775a2cd50dd320a7efabe26bc2417295c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6c2c0be67a3f6117f8b5b1197bbe8418c8c03f2309b6ff31b4cf4f31bbdf67c2
MD5 1a1910237d4a18b126834c1b4f17a4d9
BLAKE2b-256 d027aa35428e4a2bcb482df8952b60d6dd44c2140ab549bdaec91da016d64b49

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604111770834561-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 ff16cb36a45f473b0d2d6aca61c12d4574a339bf9457b350459ef9078ee14e6b
MD5 d3abe5cc0539b024b0446de7019f9416
BLAKE2b-256 39c2bf2b7c244f4d0e9fa55112cdc46649e644b2c07766f1dabee265ee1a583a

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