Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

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

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.dev202608151786444173-cp310-abi3-win_amd64.whl (3.5 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-3.0.0.9.dev202608151786444173-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.dev202608151786444173-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.dev202608151786444173-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 5a1355b0db85dd1de0cbbd42b12582d57dcaf2fdc864510d4d5167dc958b5467
MD5 8787652e46be4a0c9017a7e12ce04187
BLAKE2b-256 a724e2553053afebf663d1cadad43374e73c37297d5076710f5f739c04df1116

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 08c5e44b034ef1ff4156ccbd7183f88a5b89df5eb5231718a3ecd78f2d939921
MD5 6e838448985867901b78de18f16a1d61
BLAKE2b-256 41c586349348ebaa55fe6bece850da804b6d04267544ccd451848e70eef23e7d

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 c7fcf4d007a1a310506d33f6d0a1b944b22352f904ff89c71546fb108582a0bf
MD5 14d6510b9c3ae9771ebd536a09bd3a7e
BLAKE2b-256 88974a2cc070bfab626fe74bbbfb967de2c514ebbe3764b357a4c3e1c5c0fdc1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f8ec6dad6ef89635f0465383adc8f278fe4aa592081c79315ffedddecd193496
MD5 021c49c9c4d9835cb4ab657aa40b6782
BLAKE2b-256 cdc7ac7019f0653d91d7dcc281bbd5bb09f3dcef2b581a2174ee2c28356a7108

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-3.0.0.9.dev202608151786444173-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 039ab122ea80b0c37e52fcb358d65b60e390f5737f3a4a288f1bc8b8c1a51847
MD5 4806d9efa3f9c9f0780cbcdb427901e5
BLAKE2b-256 0613a452d4802c0c0cf25863322b8a25725944373b964bfb1a835417e27a20d4

See more details on using hashes here.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page