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.dev202507171750843886-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.1.1.9.dev202507171750843886-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.dev202507171750843886-cp310-abi3-macosx_10_15_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10+macOS 10.15+ x86-64

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 e7a894efbc04995c5a6b71212bf57554871917bdbf6c1393bef15e355cf3bb05
MD5 4320c4c0f32c91d4fc677688fc6310ce
BLAKE2b-256 0de841d28295b8118e75b56db399d97ab377bc3687ec47f9d2daa51c4a5698f2

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a2a3db283309053bd89310ee030f15ccc0ff9b800b5b28a683f884c0c8944e1a
MD5 370cc9cb8e8a6398f78a1372ceac14f9
BLAKE2b-256 f0cc63e2d035230885a7d6e669e12439760800a945a129eb8ff81af24fb7f7f8

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 757fa6d80c59cb0c65664d307dc7f9e5134b0af181e3af86fe68ff32dec9dc63
MD5 3bc45c88272414f9e2cbad8c6e2042cd
BLAKE2b-256 ae7a37db9fec168677da325b3c50df194fba808bf904485d69970c37a93cbb67

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 090f813e6e346231146756d212f2100923306c1692d28fd2f1a32085beae8509
MD5 a39bd6dd872b08f0bfd15a822f78ffa1
BLAKE2b-256 78a9b7c9a4c37b1ea7160e5491a75e6a026acfe4f87a4e830a15bdf08273173b

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.1.1.9.dev202507171750843886-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 7d137be87055bbfac93addd850e7975362d1574c4d9a2f35e53cf0ab40efa355
MD5 c29a450d238bf1344ffe7ea56d6b7b35
BLAKE2b-256 323b3d9fc223f045d83cf94e3ebd0e54debe033a3b89eef6e3d81d3f3efed0be

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