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.dev202603251770834561-cp310-abi3-win_amd64.whl (3.1 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.3.2.9.dev202603251770834561-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.dev202603251770834561-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.dev202603251770834561-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 6325fcd70ded07f78d26cabeabbefb28ecbeed9b792239c04d4040ca4e1bfcf7
MD5 1cedb7f1f63e8586561bf67c3b1f79ad
BLAKE2b-256 a9a1c7e8222b304a07fff8a4c661e835e22bed6e5a7ab76f4f539c3f28d275dd

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f229ce936b3a367c0a3525e591b223d28362f358e500009f63d60497a997f18a
MD5 48c0a49cecf605272bbf0f0bca2c7e66
BLAKE2b-256 d5bdab743c5045e4cff527296eaa4e74ca0125ea3623fe2b0ccab6f100d9e08e

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 df0a10c172a62d7708420c9ce9c150d205dd0ebae3f670c7bf2bc70ff560935c
MD5 e72142a6a74b6bb3f88444152134f346
BLAKE2b-256 2693e061ca270af4120feeb105195541c8fde0d7294a15f6db5f63b88ca3b74b

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f813c4e4886df963f906e01d634e78a3c8149e02b618f89e2c18a4e4b817ddea
MD5 8cb3ed02600efdaf74af989553638b07
BLAKE2b-256 3f16abd32f4c43eabaacb3ea9caf767a4437beb493e33c3164139a0bfb808002

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.3.2.9.dev202603251770834561-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 50f580f9fd70087d729f69c5ef3f02637398eda98b8cc3c04b99b810fb30a500
MD5 94932914bfd8f2095c26fbcab4455c8f
BLAKE2b-256 e0a27d4b51eb4716dc7e67d30f8ab1a318dc96dea47f04b2086f28a25a850f88

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