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.dev202607141783661999-cp310-abi3-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202607141783661999-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.dev202607141783661999-cp310-abi3-macosx_10_15_x86_64.whl (3.8 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 64124551052fb7d1b3acd7a3f12565da9d92479c2a55a6f228272d6fc8752593
MD5 d8d9036d6f9edb5327ba5862edc8e99c
BLAKE2b-256 f7bb44f45e7e0e07dbdb30f1de0160087aae7dbffe4fca93c6dbf473af53d846

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f75859aa42cd7a07500bfbf362c0784b89718a25e46bc79012afc84121c71aee
MD5 ac88976da6cbd3837674e44a4e86185e
BLAKE2b-256 58dfd2718ddd4e8badd4394dfd3ea8b8f4895a6dc68e3b2d40e0cf8ec6a15033

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b4cb5c4166a63fddc295117763449df9288753bfccb1d8828d7d439de6ac15e5
MD5 bbaf12658105ef8e7223e3224b91255e
BLAKE2b-256 0ec3e4a3444d47649c81328f5d73c37145eaec9930a208ad565d23688fab3079

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a55af6319e5374ddb249c5fc002e24329f7536b5c44d7f0aa437837377f7b3ab
MD5 2e07d4620404d57706058c47fae721b5
BLAKE2b-256 475ce357fd6aa04e6b8258ba6189a30e0ca8801a9fa5a575a9060e634d52a791

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202607141783661999-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 758e363d1a2a41b689f2eedfa177abeb57a047e743b1a62b525ad7ad49f8161a
MD5 a586e5c55c4148da86841e0fa14e2423
BLAKE2b-256 22bc1e9c01c46552e55392a0fbd379472dcccc8c696d8d3d9c0a0cff60815041

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