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.dev202512281765915415-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.1.9.dev202512281765915415-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.dev202512281765915415-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.dev202512281765915415-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 550ab1b33aa264a22144a443fbedcbf5c00ad239579fe4d782cac29e27906264
MD5 ae4bcd9a4c5f137bef14ea83fafb436f
BLAKE2b-256 8c29af1bcc824c70e44407ed98d260ba92240de45fd5eae0a1f518d7856401e6

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 08e6d5eb50642641b7e72861151e536f577a3efddfd52a056478a25df553deb7
MD5 c39e36313c0b0d896ef5f12159727ae0
BLAKE2b-256 a21b0e2213340efae0ade530ca37392c0a5442280cc70dd1309ce713eda2e19a

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a77fd2567a4b1ce3a86775bdef81e60f79b5eb99820458e859f59907fd37569f
MD5 925466fe77c46964c8068a7214c76952
BLAKE2b-256 ef698dcc0f97d2fe663b1423e1aec012fb19a78be1809df28a7d782987b013e7

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7c6be9013641f55f7a1db2fef230f3dd7ecec5c37cf91633b474563445032956
MD5 277713bc340fa66c8e458d2803f991d0
BLAKE2b-256 e96ef3252fbdc3a205e13d1c962a308d6de6d4f35beb0ef15fc3323636255edf

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.1.9.dev202512281765915415-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 a13d501995792c4230a8a1a532dc712422b6191fd0603bc610b2221de7147b81
MD5 681a808d213517aec52846b5f91777f5
BLAKE2b-256 d2a9023cb334007098ecdef837fdb414b1b138746075e1162ff09ee630939faf

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