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.dev202506041747485979-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.1.1.9.dev202506041747485979-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.dev202506041747485979-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.dev202506041747485979-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 41acbf1c8db3b9789fc05aded0182013a53aca39d5971aff42d8768654493463
MD5 17bab274a06f73c1ba18ec3d45b4b74d
BLAKE2b-256 02b9a34c22370eaefd17beab65d70ba8a8f6c8603697239461caae572b9bbb1d

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 c259e94d33cc05ca2953b543fafbdedb4297c5dec5503c7d8bd13a08516c61d1
MD5 143d2e71ecfbd79a099a40797f81d812
BLAKE2b-256 0b22fcaa7540d7d879810bbd4a03a403d2acd78d97c5a4257b675ddf2bc2b0c5

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b0fac018df100f2bb5e2cbdcf6d04001d401586782372d432f4f2cffb8e573d8
MD5 9f0004b873116606a1653eae57b4a388
BLAKE2b-256 8998a228e1257f8af4e4b6cf0ad1e6b3cc53714d119c22118bcfbdf1b14c097c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4fc7f225a1a11bcec1f5e1583dce2e87b5b17890e6b63b7ae6eb694ef7dcb02b
MD5 a95e851753ab25950a475d5941677474
BLAKE2b-256 8513db250de109c672dfbac9a4eab3169733869d6b96cd713b4620d90471c7f8

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202506041747485979-cp310-abi3-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 1858dbf273d0f4e255a601cf9a46cc9087d287a0f67d9ea302cbc298b1bc0914
MD5 253766ef5224682f871e03d93d0609de
BLAKE2b-256 2b351f1537b74991ac2352697befffa67a77f486142c44f90f2d93983f9282fd

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