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-3.0.0.9.dev202607201784318865-cp310-abi3-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-macosx_11_0_arm64.whl (3.4 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-3.0.0.9.dev202607201784318865-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-3.0.0.9.dev202607201784318865-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 bf0fff2871d593baacaeed0cfafa96b8cde8957c98f2f1ba6b9e2cd5f0fca740
MD5 d0e0562502c8ffae91fab2ccf5da4b9a
BLAKE2b-256 00e54f01a537bb56a5b8e56b15edd53a2706a9a509090b816ec17c12da6f688d

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 d01dcf102a4a678169620175dcfff550b3722d3aca9d4e6a01401d22ba2d2ee3
MD5 8e2a298db1ade2350037e9d825c2a6a7
BLAKE2b-256 051dfaeb0938b7dccf87ce3b64b2f31cdce4151d057a95a463353289dc8373a1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 6afec40b6385c6266c155e2144303999b1a44bc5de3b42a1390889d1bfa57d66
MD5 0f75d48f444c326c5226ac95161c4729
BLAKE2b-256 e35689eb303dd9b63de588e49c9841031d1a08420ffc00dc7e8f7d335595c97c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 db6119961d8976def621549c628820033dccee6db1c25a6a3fa7d610ec01064f
MD5 2c89cdf90cfb6dbd44c4978881f070bd
BLAKE2b-256 f464d37c5d224bf6f34945347f34777bc74c4d345697695e0dbd6bd6a969c5fc

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202607201784318865-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 743271cb49bc9fbfd688cfe1b7984b4ae17d420fc7ef7f291d1f784eb44f8fa3
MD5 9faafd3646ad21f8e69fece173b8129c
BLAKE2b-256 615ed6a77bcdc943b8f50432a7a77fb73daa8f097bf54c192569bb07546d9845

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