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-2.1.1.9.dev202505201747485979-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_10_13_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10+macOS 10.13+ x86-64

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 c1b0946881a97ae1f749ad0575889f0d00a92704e85d8d2392a5c43d6c82fcb1
MD5 183ec399b2ef98e2f76387912afb8068
BLAKE2b-256 a22afee4011b41b090df265b9143e2e5e705eec50096a32dcc72a112efe858eb

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c230fea4d77afb46666fb8b94b2e59d1e41911b6be6e4d2ddfef23828c133a25
MD5 ebfa66c0a4912c0b9643b643920baa4a
BLAKE2b-256 5e14859edb9782aa3b0b186857d11fd045044831302a525aaad0b6793d5e4719

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 e8e512194b36ff1bb6e7a7801bc4cd4d7777d7f153d63c5c34c256454efd6f20
MD5 d27fb6091827ac8d9b848d720842e9ee
BLAKE2b-256 3d66950a0b5e2df12c8ae33f9fc3ab0e194e7e98a76e83c7a8dd08ecb922ebb0

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 11cd0c3bc6fa7972546d6176aa5b1ae4b4eec3da49640ec4d32b828c4c8c1c4d
MD5 debd881aca703fef1d79154ed58bde9f
BLAKE2b-256 520def190fcffe5b994f1f781b2646a6b46ff0c92622c63ed0e4f4a27223b788

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202505201747485979-cp310-abi3-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 3326eb56f11fe8a2c8d6667c450efcb0f4cb1e3ff5a5d9ee502f2b73ae690f3e
MD5 780263a5848cee76e194c7add5404af5
BLAKE2b-256 a6c39800ba33eb690557919434fa3ea761bc7135961a5d9a747518c47bed7f76

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