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-2.2.0.9.dev202508071753652324-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.2.0.9.dev202508071753652324-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-2.2.0.9.dev202508071753652324-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 81548a80d365597220b289c32d7e6e10c8ed379606017025c567c59562eba747
MD5 e9ab4a184eebfa2b253e90a6af9ec69b
BLAKE2b-256 1fa15a0191501f7fd872473941593be19dbceb972878e1efc242ae8c4550d6f1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 4a071035b4589f847afb03c71f096ad498586212c0dd712fb76d562a2ef59cc4
MD5 d510ea558db7bb3f5857300bccf93761
BLAKE2b-256 2e71db1beef9a0d742feca9ea1df754718c75c0d2c3a807848e7f6be9fc2ef9d

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 87a14d550824afa60aa271d9757899a851a9186a41091d208817ebe16356e5df
MD5 69add77fd12922d62cb9ebdd17264c81
BLAKE2b-256 1830cb42c63669443bc454aca9145c148c3e2f6341126485d66be98a35d1b380

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6cd9ef3ecd44a397a459a03b744d06d3ac9803b6e4296d284ab058b03197c3d4
MD5 09e180a72119bd7d01e62e87713d32d7
BLAKE2b-256 f6c6831601f9dd55af0beab3fb2a892cde7e73e4fe0015dcf04ca5581ce30f44

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508071753652324-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 1596179c72160c8da655a494d7c396f0adb2663ddf3b7fc826b121ab5b567cb9
MD5 f58a69e61e04f9e3b0631d6b88488058
BLAKE2b-256 34a830354ac6034a38e211dc6f731ebc9c465c38be51de2174c5d412ee703c37

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