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.dev202603311770834561-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-macosx_11_0_arm64.whl (4.2 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.3.2.9.dev202603311770834561-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.dev202603311770834561-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 8dba7b40a86c2a733659c34d4f5b9055f13da90c2290946c64640b61b1050873
MD5 dbf576db4f09877d613cfb0f657e959e
BLAKE2b-256 b73c6a5737ca7f1efbd2da1c7e7f5e9cca6463618bb909871d7bdf363b7609c2

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 64ca13e4462c7487359794417fb55b0ad4670bf007dc0e003df446d25047dbd5
MD5 8f575dd4f079e0e2244f45a530bc3901
BLAKE2b-256 5ce5bd0222e0c132f961a4babb7fe814c44aa4205f97abfd81d5038e36b5cfe2

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 614447243fb990c1fae6b269ea314ae7b2e94a7e67812aee6ee41ef816f6c7b6
MD5 fd2d31e6f0ae6905fa83030c9d7370d9
BLAKE2b-256 d4a123c5607a2fb031268b58ce65de4fc457d1b8bf0280f0007538a54a8177ad

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 eb4502a14268bbb20d9d340e23bb1f71eb771c17e89412e8054507d548406dd1
MD5 e091d90b97f77e2c08c7a5b191b49777
BLAKE2b-256 79637397e0f23f797131c734176247b5e4639d2d0c8b3e2de79fbb139a0363f9

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603311770834561-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 8301b85e4119a11bd697ed66f67d5ac44320c43fc3af7b669772290a572b4ddc
MD5 8d12075c5643e5b66a1897a87f701bae
BLAKE2b-256 7bc6ecb72ee292e400f2376f6048e96a1b36463761607c5b1680cffa945c6496

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