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.dev202604161770834561-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_10_15_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 38eb0b20977fcc73ec6bbf978786d661550149ca238cc81edadb2e250e17a67d
MD5 5cb76ca56859251a83770acb6e5ea905
BLAKE2b-256 d535490c510a4b3276825be8d8a1b61f4677975c1a4bb70e82789377c881d076

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6b892a5834356aac5adf4b02aa4fe185b6eb83901ea53a812feb991ebe6e42fd
MD5 c4c4da857c02400a125a3a38c6029342
BLAKE2b-256 1efa93d4c325d8cd0191c12b89382e138ee296c522c9439920b3873ef42acaa6

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ce360a1008b5e1d2eb227892470647a5dfcaae8af4783d5e754cc97d8256cbed
MD5 8c387c3657b432439421ffad9b29f2e9
BLAKE2b-256 962a17ce5e67d7ea80b0c05f4780bf053573d27541bcbc3e74a62087eed12df1

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f3a84332cf00e24ac8cd5a24d8f89833393d707531fd50097ad48d161aafe60a
MD5 82e73f33e9c5d94d4bc7aca147a64669
BLAKE2b-256 d2c7463ca37309426fc168abb55c4498aca64e66cd125252d6e0f7aaeb6b2133

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202604161770834561-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 c7573d7d4adb42bc635f4664ca5973d157e224e7b7a66104a7c6b7c10492e438
MD5 5e3e0f8de9f11266574f6d2d788cf350
BLAKE2b-256 d30fb72550762d45a0107fdab294de7c1c1730f9f77dc3e6c198f74c1c24ed86

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