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-2.2.0.9.dev202508271753652324-cp310-abi3-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10+Windows x86-64

pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-macosx_11_0_arm64.whl (4.3 MB view details)

Uploaded CPython 3.10+macOS 11.0+ ARM64

pyagrum_nightly-2.2.0.9.dev202508271753652324-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-2.2.0.9.dev202508271753652324-cp310-abi3-win_amd64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-win_amd64.whl
Algorithm Hash digest
SHA256 d3db0fb67c20899ca06aa5acc62b1fdc460a0fdca2150d8a7d9aa1f39a7a6d5f
MD5 b084f9553f748cb2ffdd5b68203d0c13
BLAKE2b-256 8d43f5acf6aa17818bda70147f3cfa3b8cd1330d105c7f058740568388046947

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7050b19f9ff80f36d3c740650a481ca4c1ec62cea48b96893ea1e3bbff193822
MD5 c757d5f1a7fd3c2f6820b431e71100ea
BLAKE2b-256 6b7e74a045e00bc72a24bf3192da221efbad7bd5a921d55cbbcb530abdefcb9e

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 79dc4fc54a3533161a3b4c4cac73d9c6b30066ef315529b98f7f582f0c5731e7
MD5 5e13bfcbd3c6283a1f33bb88fa9aa950
BLAKE2b-256 b0c3a2f20da612333b0334c95bdcd759266220c4dd06773cb3eb36b957cf754c

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1d526ad7a722d75377c1da183c61189ff6fadc5c47d54a03f1aaa1958993a141
MD5 ffde95aa45878f8f31011bd5e0d5e43c
BLAKE2b-256 fda4bb4d7543f5234d153137021162d0132e03afe511d1049df10387ce3203ec

See more details on using hashes here.

File details

Details for the file pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for pyagrum_nightly-2.2.0.9.dev202508271753652324-cp310-abi3-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 f2babaeb48b46dda8d6e6bccea09d7a455c2ac1b27833543846cf4b8904284ea
MD5 16f5f4caa0284f5df492fcab86baf43a
BLAKE2b-256 67f81b750f3b7122243034e65bab6eb7074cbdfaaf73b9301d3299488cd08433

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