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Infer.NET is a framework for running Bayesian inference in graphical models

Home Page:https://dotnet.github.io/infer/

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Infer.NET

Infer.NET is a framework for running Bayesian inference in graphical models. It can also be used for probabilistic programming.

One can use Infer.NET to solve many different kinds of machine learning problems - from standard problems like classification, recommendation or clustering through to customised solutions to domain-specific problems.

Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.

Contents

Build Status

Debug Release
Windows Win Debug Win Release
Linux Linux Debug Linux Release
macOS macOS Debug macOS Release

Installation

Infer.NET runs on Windows, Linux, and macOS - any platform where .NET Core 2.1 is available.

First ensure you have installed .NET Core 2.1. Infer.NET also works on the .NET Framework 4.6.1 and above.

Once you have an app, you can install the Infer.NET NuGet package(s) from the .NET Core CLI using:

dotnet add package Microsoft.ML.Probabilistic
dotnet add package Microsoft.ML.Probabilistic.Compiler
dotnet add package Microsoft.ML.Probabilistic.Learners

or from the NuGet package manager:

Install-Package Microsoft.ML.Probabilistic
Install-Package Microsoft.ML.Probabilistic.Compiler
Install-Package Microsoft.ML.Probabilistic.Learners
Install-Package Microsoft.ML.Probabilistic.Visualizers.Windows

Or alternatively you can add the Microsoft.ML.Probabilistic.* package(s) from within Visual Studio's NuGet package manager or via Paket.

There currently are four maintained Infer.NET nuget packages:

  1. Microsoft.ML.Probabilistic contains classes and methods needed to execute the inference code.
  2. Microsoft.ML.Probabilistic.Compiler contains the Infer.NET Compiler, which takes model descriptions written using the Infer.NET API and converts them into inference code. It also contains utilities for the visualization of the generated code.
  3. Microsoft.ML.Probabilistic.Learners contains complete machine learning applications including a classifier and a recommender system.
  4. Microsoft.ML.Probabilistic.Visualizers.Windows contains an alternative .NET Framework and Windows specific set of visualization tools for exploring and analyzing models.

Documentation

Documentation can be found on the Infer.NET website.

Structure of Repository

  • The Visual Studio solution Infer2.sln in the root of the repository contains all Infer.NET components, unit tests and sample programs from the folders described below.

  • src/

    • Compiler contains the Infer.NET Compiler project which takes model descriptions written using the Infer.NET API, and converts them into inference code. The project also contains utility methods for visualization of the generated code.

    • Csoft is an experimental feature that allows to express probabilistic models in a subset of the C# language. You can find many unit tests of Csoft models in the Tests project marked with Category: CsoftModel trait.

    • Examples contains C# projects that illustrate how to use Infer.NET to solve a variety of different problems.

      • ClickThroughModel - a web search example of converting a sequence of clicks by the user into inferences about the relevance of documents.

      • ClinicalTrial - the clinical trial tutorial example with an interactive user interface.

      • InferNET101 - samples from Infer.NET 101 introduction to the basics of Microsoft Infer.NET programming.

      • ImageClassifier - an image search example of classifying tagged images.

      • LDA - this example provides Infer.NET implementations of the popular LDA model for topic modeling. The implementations pay special attention to scalability with respect to vocabulary size, and with respect to the number of documents. As such, they provide good examples for how to scale Infer.NET models in general.

      • MontyHall - an Infer.NET implementation of the Monty Hall problem, along with a graphical user interface.

      • MotifFinder - an Infer.NET implementation of a simple model for finding motifs in nucleotide sequences, which constitutes an important problem in bioinformatics.

    • FSharpWrapper is a wrapper project that hides some of the generic constructs in the Infer.NET API allowing simpler calls to the Infer.NET API from standard F#.

    • IronPythonWrapper contains wrapper for calling Infer.NET from the IronPython programming language and tests for the wrapper. Please refer to README.md for more information.

    • Learners folder contains Visual Studio projects for complete machine learning applications including classification and recommendation. You can read more about Learners here.

    • Runtime - is a C# project with classes and methods needed to execute the inference code.

    • Tutorials contains Examples Browser project with simple examples that provide a step-by-step introduction to Infer.NET.

    • Visualizers/Windows contains an alternative .NET Framework and Windows specific set of visualization tools for exploring and analyzing the code generated by the Compiler.

  • test/

    • TestApp contains C# console application for quick invocation and debugging of various Infer.NET components.

    • TestFSharp is an F# console project for smoke testing of Infer.NET F# wrapper.

    • TestPublic contains scenario tests for tutorial code. These tests are a part of the PR and nightly builds.

    • Tests - main unit test project containing thousands of tests. These tests are a part of the PR and nightly builds. The folder Tests\Vibes contains MATLab scripts that compare Infer.NET to the VIBES package. Running them requires Vibes2_0.jar (can be obtained on the VIBES website) to be present in the same folder.

    • Learners folder contains the unit tests and the test application for Learners (see above).

  • build folder contains the YAML definitions for the Continuous Integration builds and the specification files for the nuget packages.

  • docs folder contains the scripts for building API documentation and for updating https://dotnet.github.io/infer. Please refer to README.md for more details.

Build and Test

Infer.NET is cross platform and supports .NET Framework 4.6.1, .NET Core 2.1, and Mono 5.0. Unit tests are written using the XUnit framework.

All of the Infer.NET libraries target .NET Standard 2.0. Projects that produce executables (including test projects) mostly target .NET Framework 4.6.1, .NET Core 2.1, or both depending on build configuration:

Configurations Targeted Frameworks
Debug, Release both .NET Framework 4.6.1 and .NET Core 2.1
DebugFull, ReleaseFull .NET Framework 4.6.1 only
DebugCore, ReleaseCore .NET Core 2.1 only

Windows

Prerequisites

Visual Studio 2017. If you don't have Visual Studio 2017, you can install the free Visual Studio 2017 Community.

Build and test

You can load Infer2.sln solution located in the root of repository into Visual Studio and build all libraries and samples.

NB! The solution has a number of build configurations that allows building either for all supported frameworks simultaneously or only for a specific one, but in order for Visual Studio to behave correctly, the solution needs to be closed and re-opened after switching between such configurations.

Unit tests are available in Test Explorer window. Normally you should see tests from 3 projects: Tests, PublicTests and LearnersTest. Note, that some of the tests are categorized, and those falling in the OpenBug or BadTest categories are not supposed to succeed.

Linux and macOS

All components of Infer.NET and almost all sample projects run on .NET Core 2.1 and/or Mono except sample applications that use WPF.

Prerequisites

  • .NET Core 2.1 SDK to build and run .NET Standard and .NET Core projects

    and, optionally,

  • Mono (version 5.0 and higher) and NuGet package manager to build and run .NET Framework 4.6.1 projects that don't use WPF (there're some examples that use Win Forms and, therefore, don't run on .NET Core, but can be built and run with Mono; there's also Visualizers/Windows project mentioned above that can be built with Mono using /p:MonoSupport=true)

Build

To build .NET Standard libraries and .NET Core executables, run in the root of the repository either

dotnet build -c DebugCore Infer2.sln

to build debug assemblies, or

dotnet build -c ReleaseCore Infer2.sln

to build release assemblies.

The corresponding commands to build .NET Standard libraries and .NET Framework executables with Mono are

msbuild /c:DebugFull /p:MonoSupport=true /restore Infer2.sln

and

msbuild /c:ReleaseFull /p:MonoSupport=true /restore Infer2.sln

Please, expect build failure messages about examples that use WPF GUI. Libraries and executables that don't reference WPF should build, though.

Run unit tests

In order to run unit tests, build the test project and execute one of the following commands:

dotnet ~/.nuget/packages/xunit.runner.console/2.3.1/tools/netcoreapp2.1/xunit.console.dll <path to netcoreapp2.1 assembly with tests> <filter>
mono ~/.nuget/packages/xunit.runner.console/2.3.1/tools/net452/xunit.console.exe <path to net461 assembly with tests> <filter>

There are three test assemblies in the solution:

  • Microsoft.ML.Probabilistic.Tests.dll in the folder test/Tests.
  • TestPublic.dll in the folder test/TestPublic.
  • Microsoft.ML.Probabilistic.Learners.Tests.dll in the folder test/Learners/LearnersTests.

Depending on the build configuration and targeted framework, the assemblies will be located in the bin/Debug<Core|Full>/<netcoreapp2.1|net461> or bin/Release<Core|Full>/<netcoreapp2.1|net461> subdirectories of the test project.

<filter> is a rule to choose what tests will be run. You can specify them using -trait Category=<category> and -notrait Category=<category> parts of <filter>. The former selects tests of the given category, while the latter selects test that don't belong to the given category. These can be combined: several -trait options mean that at least one of the listed traits has to be present, while several -notrait options mean that none of such traits can be present on the filtered tests.

Runner executes tests in parallel by default. However, some test category must be run sequentially. Such categories are:

  • Performance
  • DistributedTest
  • CsoftModel
  • ModifiesGlobals

Add the -parallel none argument to run them.

CompilerOptionsTest is a category for long running tests, so, for quick testing you must filter these out by -notrait. BadTest is a category of tests that must fail. OpenBug is a category of tests that can fail.

An example of quick testing of Microsoft.ML.Probabilistic.Tests.dll in Debug configuration after changing working directory to the Tests project looks like:

dotnet ~/.nuget/packages/xunit.runner.console/2.3.1/tools/netcoreapp2.1/xunit.console.dll bin/DebugCore/netcoreapp2.1/Microsoft.ML.Probabilistic.Tests.dll -notrait Category=OpenBug -notrait Category=BadTest -notrait Category=CompilerOptionsTest -notrait Category=CsoftModel -notrait Category=ModifiesGlobals -notrait Category=DistributedTest -notrait Category=Performance

dotnet ~/.nuget/packages/xunit.runner.console/2.3.1/tools/netcoreapp2.1/xunit.console.dll bin/DebugCore/netcoreapp2.1/Microsoft.ML.Probabilistic.Tests.dll -trait Category=CsoftModel -trait Category=ModifiesGlobals -trait Category=DistributedTests -trait Category=Performance -notrait Category=OpenBug -notrait Category=BadTest -notrait Category=CompilerOptionsTest -parallel none

To run the same set of tests on Mono:

mono ~/.nuget/packages/xunit.runner.console/2.3.1/tools/net452/xunit.console.exe bin/DebugFull/net461/Microsoft.ML.Probabilistic.Tests.dll -notrait Category=OpenBug -notrait Category=BadTest -notrait Category=CompilerOptionsTest -notrait Category=CsoftModel -notrait Category=ModifiesGlobals -notrait Category=DistributedTest -notrait Category=Performance

mono ~/.nuget/packages/xunit.runner.console/2.3.1/tools/net452/xunit.console.exe bin/DebugFull/net461/Microsoft.ML.Probabilistic.Tests.dll -trait Category=CsoftModel -trait Category=ModifiesGlobals -trait Category=DistributedTests -trait Category=Performance -notrait Category=OpenBug -notrait Category=BadTest -notrait Category=CompilerOptionsTest -parallel none

Helper scripts netcoretest.sh and monotest.sh for running unit tests on .NET Core and Mono respectively are located in the test folder.

Contributing

We welcome contributions! Please review our contribution guide.

License

Infer.NET is licensed under the MIT license.

.NET Foundation

Infer.NET is a .NET Foundation project. It's also a part of ML.NET machine learning framework.

There are many .NET related projects on GitHub.

  • .NET home repo - links to 100s of .NET projects, from Microsoft and the community.

About

Infer.NET is a framework for running Bayesian inference in graphical models

https://dotnet.github.io/infer/

License:MIT License

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