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-2.3.0.9.dev202511131762170379-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-macosx_11_0_arm64.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.0.9.dev202511131762170379-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-2.3.0.9.dev202511131762170379-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 87ee82a6b45ac63731b0be246e56131044592e0202455c078a276a40b4a044ef
MD5 0f935694f38455895b1bc346d81e1b76
BLAKE2b-256 803a890d642b109c8143576d24b2881132b961284404dad3e620f439a4187089

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e3ef32789ab8dcb23d07f604ff28b026cd6c3fe202dedd53e2f76f879a7c8b52
MD5 ff2335c1f76e399e0da7cf49341161f8
BLAKE2b-256 37cec2cd3bb32f3b138efc6d3836d12a8d0d6665c5da97b6b7fa413605eabeb1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 74ba549b78621371a276b3d04ff876132627845e18eb1efc677a9c6e455434df
MD5 7bbd6354eb6797d3d198a3df358d30c9
BLAKE2b-256 04b5adfbdf4edfbe56c9ab7567e01f07f8ab041d87e7efcb14efd2bcb7578527

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f5d59f0684b711b95ce9e8e6a3fd2279935a27224469bd7bcd2d4ec64c20a409
MD5 6bdc9c3b9942506055353bde4ff0976f
BLAKE2b-256 8bfb521cc1306fd4b3077de2882882b9cebe6cd515dec84aa3eab77fa6f23e30

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.0.9.dev202511131762170379-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 6bb8cbaf3031761ea7172d0cdfe61a156fe523085053195a082e1765ff19b690
MD5 2cdac89c02877e72d664d140931e2b54
BLAKE2b-256 656d7510b9a07a68e7fedd04466e48bb3e143f1fc3c14300420f542f00a20c69

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