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.dev202601021765915415-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.1.9.dev202601021765915415-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.dev202601021765915415-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.dev202601021765915415-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 aa85641f22fc87bacfd4508a699bc2fc239a76f492ee23d567d67f1ba027ccfe
MD5 2e10987d3aa92de7e1f1853a922bee69
BLAKE2b-256 e30b6171484c6c1bfd55a3dbf6ccbf8478e7745edc362b7789ed8babb5b24f95

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 01891cb2c21125c32a303a787e98538b24ad3b64ed45db240b41b17f26bc65d3
MD5 0610993f82fb503df95f799737436dc3
BLAKE2b-256 6f4cbcca103802e6011bed21c2f732772000818046f9f407f761c09090dc4fd8

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e838f6ca0f617e5cc077d7429e2cc859efedb26dcd095f564964cf08a74548b9
MD5 2c65ab1e3a081d3172b2abc64802cc3a
BLAKE2b-256 a093afaacf2086101487bdcf6150775960c439a157687b5bcc12c8cbfba9fbd9

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f250fe980a0b70b0276f1f70ea62681a5ff72c16e6ef0b6e67ce77e68c8f8eff
MD5 36e07a2a33b59e0d01b7bba230709665
BLAKE2b-256 c5af2105e03ebfa7ad84add6aa81d0f1e267f15ad2bb7755cf5153b1e1ccc6f1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202601021765915415-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 4c1ffe5e82cfccc89f763c2acbb0244533b8dfdcbb3baf9fcfbaa7081c8c2216
MD5 8be18d047a246de65272b80634ee0136
BLAKE2b-256 9fcd9374d2cdfc807d1323eb3fc3b8771543eca914b74d81291a0693d8d5ce05

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