diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index c2e0e2c0d4..4e18da7c90 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -11,5 +11,3 @@ phases: buildScript: build.cmd queue: name: Hosted VS2017 - demands: - - agent.os -equals Windows_NT diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index da15d0493f..321ad3dcf0 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ If you are new to GitHub [here](https://help.github.com/categories/collaborating As a first time contributor, you will be invited to sign the Contributor License Agreement (CLA). Please follow the instructions of the dotnet foundation bot reviewer on your PR to sign the agreement indicating that you have appropriate rights to your contribution. -Your pull request needs to reference a filed issue. Please fill in the template that is populated for the pull request. Only pull requests adressing small typos can have no issues associated with them. +Your pull request needs to reference a filed issue. Please fill in the template that is populated for the pull request. Only pull requests addressing small typos can have no issues associated with them. An ML.NET team member will be assigned to your pull request once the continuous integration checks have passed successfully. diff --git a/Directory.Build.props b/Directory.Build.props index 73144201c7..bdca231554 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -114,4 +114,11 @@ true + + $(Configuration.EndsWith('-Intrinsics')) + + + + $(RepoRoot)build\AfterCommonTargets.targets + diff --git a/Microsoft.ML.sln b/Microsoft.ML.sln index 58e24041f1..18d9d3867e 100644 --- a/Microsoft.ML.sln +++ b/Microsoft.ML.sln @@ -97,6 +97,19 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CodeAnalyzer", EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CodeAnalyzer.Tests", "test\Microsoft.ML.CodeAnalyzer.Tests\Microsoft.ML.CodeAnalyzer.Tests.csproj", "{3E4ABF07-7970-4BE6-B45B-A13D3C397545}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CpuMath.PerformanceTests", "test\Microsoft.ML.CpuMath.PerformanceTests\Microsoft.ML.CpuMath.PerformanceTests.csproj", "{7333EDEF-4144-405C-A5EC-6F42201857D8}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CpuMath.UnitTests.netstandard", "test\Microsoft.ML.CpuMath.UnitTests.netstandard\Microsoft.ML.CpuMath.UnitTests.netstandard.csproj", "{A0E562A9-0E6D-470D-B180-6EB44BA84D60}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.CpuMath.UnitTests.netcoreapp", "test\Microsoft.ML.CpuMath.UnitTests.netcoreapp\Microsoft.ML.CpuMath.UnitTests.netcoreapp.csproj", "{5F81A2A4-73AD-494C-B387-07D605EC8826}" +EndProject + +Project("{F2A71F9B-5D33-465A-A702-920D77279786}") = "Microsoft.ML.FSharp.Tests", "test\Microsoft.ML.FSharp.Tests\Microsoft.ML.FSharp.Tests.fsproj", "{802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.ImageAnalytics", "src\Microsoft.ML.ImageAnalytics\Microsoft.ML.ImageAnalytics.csproj", "{00E38F77-1E61-4CDF-8F97-1417D4E85053}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.HalLearners", "src\Microsoft.ML.HalLearners\Microsoft.ML.HalLearners.csproj", "{A7222F41-1CF0-47D9-B80C-B4D77B027A61}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -329,6 +342,54 @@ Global {3E4ABF07-7970-4BE6-B45B-A13D3C397545}.Release|Any CPU.Build.0 = Release|Any CPU {3E4ABF07-7970-4BE6-B45B-A13D3C397545}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU {3E4ABF07-7970-4BE6-B45B-A13D3C397545}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Debug|Any CPU.Build.0 = Debug|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Release|Any CPU.ActiveCfg = Release|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Release|Any CPU.Build.0 = Release|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {7333EDEF-4144-405C-A5EC-6F42201857D8}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Debug|Any CPU.Build.0 = Debug|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Release|Any CPU.ActiveCfg = Release|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Release|Any CPU.Build.0 = Release|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {A0E562A9-0E6D-470D-B180-6EB44BA84D60}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Debug|Any CPU.Build.0 = Debug|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Release|Any CPU.ActiveCfg = Release|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Release|Any CPU.Build.0 = Release|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {5F81A2A4-73AD-494C-B387-07D605EC8826}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Debug|Any CPU.Build.0 = Debug|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Release|Any CPU.ActiveCfg = Release|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Release|Any CPU.Build.0 = Release|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Debug|Any CPU.Build.0 = Debug|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Release|Any CPU.ActiveCfg = Release|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Release|Any CPU.Build.0 = Release|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {00E38F77-1E61-4CDF-8F97-1417D4E85053}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Debug|Any CPU.Build.0 = Debug|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Debug-Intrinsics|Any CPU.ActiveCfg = Debug|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Debug-Intrinsics|Any CPU.Build.0 = Debug|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Release|Any CPU.ActiveCfg = Release|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Release|Any CPU.Build.0 = Release|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Release-Intrinsics|Any CPU.ActiveCfg = Release|Any CPU + {A7222F41-1CF0-47D9-B80C-B4D77B027A61}.Release-Intrinsics|Any CPU.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -365,8 +426,14 @@ Global {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {BF66A305-DF10-47E4-8D81-42049B149D2B} = {D3D38B03-B557-484D-8348-8BADEE4DF592} + {7333EDEF-4144-405C-A5EC-6F42201857D8} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {A0E562A9-0E6D-470D-B180-6EB44BA84D60} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {5F81A2A4-73AD-494C-B387-07D605EC8826} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {B4E55B2D-2A92-46E7-B72F-E76D6FD83440} = {7F13E156-3EBA-4021-84A5-CD56BA72F99E} {3E4ABF07-7970-4BE6-B45B-A13D3C397545} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {802233D6-8CC0-46AD-9F23-FEE1E9AED9B3} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {00E38F77-1E61-4CDF-8F97-1417D4E85053} = {09EADF06-BE25-4228-AB53-95AE3E15B530} + {A7222F41-1CF0-47D9-B80C-B4D77B027A61} = {09EADF06-BE25-4228-AB53-95AE3E15B530} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D} diff --git a/README.md b/README.md index 9ccd06c165..e9d256352b 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ Along with these ML capabilities this first release of ML.NET also brings the fi ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit [.NET Core](https://github.com/dotnet/core) or later is available. -The current release is 0.3. Check out the [release notes](docs/release-notes/0.3/release-0.3.md). +The current release is 0.4. Check out the [release notes](docs/release-notes/0.4/release-0.4.md). First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process. diff --git a/build.proj b/build.proj index 77b82dea2c..c9be14e930 100644 --- a/build.proj +++ b/build.proj @@ -34,24 +34,26 @@ BuildNative; $(TraversalBuildDependsOn); DownloadExternalTestFiles; - RunTests; + Targets="Restore" + Properties="MSBuildWarningsAsMessages=NU1503" /> - + + DependsOnTargets="CreateOrUpdateCurrentVersionFile;RestoreProjects"> @@ -77,7 +79,7 @@ TreatErrorsAsWarnings="true"/> - + diff --git a/build/AfterCommonTargets.targets b/build/AfterCommonTargets.targets new file mode 100644 index 0000000000..cba4c80b5c --- /dev/null +++ b/build/AfterCommonTargets.targets @@ -0,0 +1,13 @@ + + + $(MSBuildAllProjects);$(MSBuildThisFileFullPath) + + + + + \ No newline at end of file diff --git a/build/BranchInfo.props b/build/BranchInfo.props index 193aff7d35..b6d49773ec 100644 --- a/build/BranchInfo.props +++ b/build/BranchInfo.props @@ -1,7 +1,7 @@ 0 - 4 + 5 0 preview diff --git a/build/Dependencies.props b/build/Dependencies.props index 5325011f05..79ae31c598 100644 --- a/build/Dependencies.props +++ b/build/Dependencies.props @@ -8,5 +8,7 @@ 4.3.0 1.0.0-beta-62824-02 2.1.2.2 + 0.0.0.5 + 4.5.0 diff --git a/build/Empty.targets b/build/Empty.targets new file mode 100644 index 0000000000..72abf9cd60 --- /dev/null +++ b/build/Empty.targets @@ -0,0 +1,29 @@ + + + $(MSBuildAllProjects);$(MSBuildThisFileFullPath) + + ignore.targets + + + + + + + + + + + + \ No newline at end of file diff --git a/build/ci/phase-template.yml b/build/ci/phase-template.yml index bd326afb69..037c207dbb 100644 --- a/build/ci/phase-template.yml +++ b/build/ci/phase-template.yml @@ -13,16 +13,18 @@ phases: ${{ if ne(parameters.dockerImage, '') }}: _PREVIEW_VSTS_DOCKER_IMAGE: ${{ parameters.dockerImage }} queue: - parallel: 2 + parallel: 99 matrix: Build_Debug: _configuration: Debug Build_Release: - _configuration: Release + _configuration: Release ${{ insert }}: ${{ parameters.queue }} steps: + - script: $(_buildScript) -$(_configuration) + displayName: Build - script: $(_buildScript) -$(_configuration) -runtests - displayName: Build and Test + displayName: Run Tests - task: PublishTestResults@2 displayName: Publish Test Results condition: succeededOrFailed() diff --git a/build/vsts-ci.yml b/build/vsts-ci.yml index 5998c341db..1e3d601704 100644 --- a/build/vsts-ci.yml +++ b/build/vsts-ci.yml @@ -10,7 +10,7 @@ phases: DOTNET_SKIP_FIRST_TIME_EXPERIENCE: 1 DOTNET_MULTILEVEL_LOOKUP: 0 queue: - name: DotNetCore-Test + name: DotNet-Build demands: - agent.os -equals linux steps: diff --git a/config.json b/config.json index 04ff73c8b7..8436586e61 100644 --- a/config.json +++ b/config.json @@ -67,8 +67,8 @@ "defaultValue": "" }, "RunTests": { - "description": "Run tests after building.", - "valueType": "property", + "description": "MsBuild target that run the tests. Call this after building.", + "valueType": "target", "values": [], "defaultValue": "" }, @@ -113,9 +113,9 @@ } }, "runtests": { - "description": "Runs the tests after building.", + "description": "Runs the tests. Call this after building.", "settings": { - "RunTests": "true" + "RunTests": "default" } }, "verbose": { diff --git a/docs/building/windows-instructions.md b/docs/building/windows-instructions.md index 4b58d1c09b..9ffc9c82ae 100644 --- a/docs/building/windows-instructions.md +++ b/docs/building/windows-instructions.md @@ -46,14 +46,14 @@ You can use the Developer Command Prompt, Powershell or work in any regular cmd. From a (non-admin) Command Prompt window: - `build.cmd` - builds the assemblies -- `build.cmd -runTests` - builds the assemblies and runs tests +- `build.cmd -runTests` - called after a normal "build.cmd" will run all tests - `build.cmd -buildPackages` called after a normal “build.cmd” will create the NuGet packages with the assemblies in “bin" **Note**: Before working on individual projects or test projects you **must** run `build.cmd` from the root once before beginning that work. It is also a good idea to run `build.cmd` whenever you pull a large set of unknown changes into your branch. ### Running tests from the command line -From the root, use `build.cmd -runTests`. +From the root, run `build.cmd` and then `build.cmd -runTests`. For more details, or to test an individual project, you can navigate to the test project directory and then use `dotnet test` ### Running tests from Visual Studio diff --git a/docs/project-docs/developer-guide.md b/docs/project-docs/developer-guide.md index 60788f8e96..074ea03dab 100644 --- a/docs/project-docs/developer-guide.md +++ b/docs/project-docs/developer-guide.md @@ -32,7 +32,8 @@ build.cmd -Release -TargetArchitecture:x64 - Building the src and then building and running the tests ``` -build.cmd -RunTests +build.cmd +build.cmd -runTests ``` ### Building individual projects diff --git a/docs/release-notes/0.4/release-0.4.md b/docs/release-notes/0.4/release-0.4.md new file mode 100644 index 0000000000..41c436f14e --- /dev/null +++ b/docs/release-notes/0.4/release-0.4.md @@ -0,0 +1,88 @@ +# ML.NET 0.4 Release Notes + +Today we are releasing ML.NET 0.4. During this release we have started +exploring new APIs for ML.NET that enable functionality that is missing from +the current APIs. We welcome feedback and contributions to the +conversation (relevant issues can be found [here](https://github.com/dotnet/machinelearning/projects/4)). While the +focus has been on designing the new APIs, we have also moved several +components from the internal codebase to ML.NET. + +### Installation + +ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET +Core +2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md) +for more details. + +You can install ML.NET NuGet from the CLI using: +``` +dotnet add package Microsoft.ML +``` + +From package manager: +``` +Install-Package Microsoft.ML +``` + +### Release Notes + +Below are some of the highlights from this release. + +* Added SymSGD learner for binary classification + ([#624](https://github.com/dotnet/machinelearning/pull/624)) + + * [SymSGD](https://arxiv.org/abs/1705.08030) is a technique for + parallelizing + [SGD](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) + (Stochastic Gradient Descent). This enables it to sometimes perform + faster than existing SGD implementations (e.g. [Hogwild + SGD](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.trainers.stochasticgradientdescentbinaryclassifier?view=ml-dotnet)). + * SymSGD is available for binary classification, but can be used in + multiclass classification with + [One-Versus-All](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.models.oneversusall?view=ml-dotnet) + * SymSGD requires adding the Microsoft.ML.HalLearners NuGet package to your project + * The current implementation in ML.NET does not yet have multi-threading + enabled due to build system limitations (tracked by + [#655](https://github.com/dotnet/machinelearning/issues/655)), but + SymSGD can still be helpful in scenarios where you want to try many + different learners and limit each of them to a single thread. + * Documentation can be found + [here](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.trainers.symsgdbinaryclassifier?view=ml-dotnet) + +* Added Word Embeddings Transform for text scenarios + ([#545](https://github.com/dotnet/machinelearning/pull/545)) + + * [Word embeddings](https://en.wikipedia.org/wiki/Word_embedding) is a + technique for mapping words or phrases to numeric vectors of relatively low + dimension (in comparison with the high dimensional n-gram extraction). + These numeric vectors are intended to capture some of the meaning of the + words so they can be used for training a better model. As an example, + SSWE (Sentiment-Specific Word Embedding) can be useful for sentiment + related tasks. + * This transform enables using pretrained models to get the embeddings + (i.e. the embeddings are already trained and available for use). + * Several options for pretrained embeddings are available: + [GloVe](https://nlp.stanford.edu/projects/glove/), + [fastText](https://en.wikipedia.org/wiki/FastText), and + [SSWE](http://anthology.aclweb.org/P/P14/P14-1146.pdf). The pretrained model is downloaded automatically on first use. + * Documentation can be found + [here](https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.transforms.wordembeddings?view=ml-dotnet). + +* Improved support for F# by allowing use of property-based row classes ([#616](https://github.com/dotnet/machinelearning/pull/616)) + + * ML.NET now supports F# record types. + * The ML.NET samples repository is being updated to include F# samples as part of [#36](https://github.com/dotnet/machinelearning-samples/pull/36). + +Additional issues closed in this milestone can be found +[here](https://github.com/dotnet/machinelearning/milestone/3?closed=1). + +### Acknowledgements + +Shoutout to [dsyme](https://github.com/dsyme), +[SolyarA](https://github.com/SolyarA), +[dan-drews](https://github.com/dan-drews), +[bojanmisic](https://github.com/bojanmisic), +[jwood803](https://github.com/jwood803), +[sharwell](https://github.com/sharwell), +[JoshuaLight](https://github.com/JoshuaLight), and the ML.NET team for their +contributions as part of this release! \ No newline at end of file diff --git a/netci.groovy b/netci.groovy index b955bf669f..7c1126aff9 100644 --- a/netci.groovy +++ b/netci.groovy @@ -16,6 +16,7 @@ def branch = GithubBranchName def newJob = job(Utilities.getFullJobName(project, jobName, isPR)) { steps { + shell("./build.sh -$config") shell("./build.sh -$config -runtests") shell("./build.sh -buildPackages") } diff --git a/pkg/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.nupkgproj b/pkg/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.nupkgproj index 918729d99d..18cec880f9 100644 --- a/pkg/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.nupkgproj +++ b/pkg/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.nupkgproj @@ -11,4 +11,8 @@ + + + + diff --git a/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.nupkgproj b/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.nupkgproj new file mode 100644 index 0000000000..132b995a2f --- /dev/null +++ b/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.nupkgproj @@ -0,0 +1,17 @@ + + + + netstandard2.0 + ML.NET additional learners making use of hardware acceleration. They use Intel Mkl. + + + + + + + + + + + + diff --git a/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.symbols.nupkgproj b/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.symbols.nupkgproj new file mode 100644 index 0000000000..248ae82414 --- /dev/null +++ b/pkg/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.symbols.nupkgproj @@ -0,0 +1,5 @@ + + + + + diff --git a/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.nupkgproj b/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.nupkgproj new file mode 100644 index 0000000000..8bdef45d07 --- /dev/null +++ b/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.nupkgproj @@ -0,0 +1,13 @@ + + + + netstandard2.0 + ML.NET component for Image support + + + + + + + + diff --git a/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.symbols.nupkgproj b/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.symbols.nupkgproj new file mode 100644 index 0000000000..b36800ea0b --- /dev/null +++ b/pkg/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.symbols.nupkgproj @@ -0,0 +1,5 @@ + + + + + diff --git a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj index fc409ae21f..7757e264b6 100644 --- a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj +++ b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj @@ -15,6 +15,7 @@ + diff --git a/pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.props b/pkg/common/CommonPackage.props similarity index 100% rename from pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.props rename to pkg/common/CommonPackage.props diff --git a/src/Microsoft.ML.Api/ApiUtils.cs b/src/Microsoft.ML.Api/ApiUtils.cs index 8b8cb5871b..96e821f16e 100644 --- a/src/Microsoft.ML.Api/ApiUtils.cs +++ b/src/Microsoft.ML.Api/ApiUtils.cs @@ -51,14 +51,31 @@ private static OpCode GetAssignmentOpCode(Type t) /// internal static Delegate GeneratePeek(InternalSchemaDefinition.Column column) { - var fieldInfo = column.FieldInfo; - Type fieldType = fieldInfo.FieldType; - - var assignmentOpCode = GetAssignmentOpCode(fieldType); - Func func = GeneratePeek; - var methInfo = func.GetMethodInfo().GetGenericMethodDefinition() - .MakeGenericMethod(typeof(TOwn), typeof(TRow), fieldType); - return (Delegate)methInfo.Invoke(null, new object[] { fieldInfo, assignmentOpCode }); + switch (column.MemberInfo) + { + case FieldInfo fieldInfo: + Type fieldType = fieldInfo.FieldType; + + var assignmentOpCode = GetAssignmentOpCode(fieldType); + Func func = GeneratePeek; + var methInfo = func.GetMethodInfo().GetGenericMethodDefinition() + .MakeGenericMethod(typeof(TOwn), typeof(TRow), fieldType); + return (Delegate)methInfo.Invoke(null, new object[] { fieldInfo, assignmentOpCode }); + + case PropertyInfo propertyInfo: + Type propertyType = propertyInfo.PropertyType; + + var assignmentOpCodeProp = GetAssignmentOpCode(propertyType); + Func funcProp = GeneratePeek; + var methInfoProp = funcProp.GetMethodInfo().GetGenericMethodDefinition() + .MakeGenericMethod(typeof(TOwn), typeof(TRow), propertyType); + return (Delegate)methInfoProp.Invoke(null, new object[] { propertyInfo, assignmentOpCodeProp }); + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + + } } private static Delegate GeneratePeek(FieldInfo fieldInfo, OpCode assignmentOpCode) @@ -81,6 +98,28 @@ private static Delegate GeneratePeek(FieldInfo fieldInfo, Op return mb.CreateDelegate(typeof(Peek)); } + private static Delegate GeneratePeek(PropertyInfo propertyInfo, OpCode assignmentOpCode) + { + // REVIEW: It seems like we really should cache these, instead of generating them per cursor. + Type[] args = { typeof(TOwn), typeof(TRow), typeof(long), typeof(TValue).MakeByRefType() }; + var mb = new DynamicMethod("Peek", null, args, typeof(TOwn), true); + var il = mb.GetILGenerator(); + var minfo = propertyInfo.GetGetMethod(); + var opcode = (minfo.IsVirtual || minfo.IsAbstract) ? OpCodes.Callvirt : OpCodes.Call; + + il.Emit(OpCodes.Ldarg_3); // push arg3 + il.Emit(OpCodes.Ldarg_1); // push arg1 + il.Emit(opcode, minfo); // call [stack top].get_[propertyInfo]() + // Stobj needs to coupled with a type. + if (assignmentOpCode == OpCodes.Stobj) // [stack top-1] = [stack top] + il.Emit(assignmentOpCode, propertyInfo.PropertyType); + else + il.Emit(assignmentOpCode); + il.Emit(OpCodes.Ret); // ret + + return mb.CreateDelegate(typeof(Peek)); + } + /// /// Each of the specialized 'poke' methods sets the appropriate field value of an instance of T /// to the provided value. So, the call is 'peek(userObject, providedValue)' and the logic is @@ -88,14 +127,30 @@ private static Delegate GeneratePeek(FieldInfo fieldInfo, Op /// internal static Delegate GeneratePoke(InternalSchemaDefinition.Column column) { - var fieldInfo = column.FieldInfo; - Type fieldType = fieldInfo.FieldType; - - var assignmentOpCode = GetAssignmentOpCode(fieldType); - Func func = GeneratePoke; - var methInfo = func.GetMethodInfo().GetGenericMethodDefinition() - .MakeGenericMethod(typeof(TOwn), typeof(TRow), fieldType); - return (Delegate)methInfo.Invoke(null, new object[] { fieldInfo, assignmentOpCode }); + switch (column.MemberInfo) + { + case FieldInfo fieldInfo: + Type fieldType = fieldInfo.FieldType; + + var assignmentOpCode = GetAssignmentOpCode(fieldType); + Func func = GeneratePoke; + var methInfo = func.GetMethodInfo().GetGenericMethodDefinition() + .MakeGenericMethod(typeof(TOwn), typeof(TRow), fieldType); + return (Delegate)methInfo.Invoke(null, new object[] { fieldInfo, assignmentOpCode }); + + case PropertyInfo propertyInfo: + Type propertyType = propertyInfo.PropertyType; + + var assignmentOpCodeProp = GetAssignmentOpCode(propertyType); + Func funcProp = GeneratePoke; + var methInfoProp = funcProp.GetMethodInfo().GetGenericMethodDefinition() + .MakeGenericMethod(typeof(TOwn), typeof(TRow), propertyType); + return (Delegate)methInfoProp.Invoke(null, new object[] { propertyInfo }); + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + } } private static Delegate GeneratePoke(FieldInfo fieldInfo, OpCode assignmentOpCode) @@ -115,5 +170,20 @@ private static Delegate GeneratePoke(FieldInfo fieldInfo, Op il.Emit(OpCodes.Ret); // ret return mb.CreateDelegate(typeof(Poke), null); } + + private static Delegate GeneratePoke(PropertyInfo propertyInfo) + { + Type[] args = { typeof(TOwn), typeof(TRow), typeof(TValue) }; + var mb = new DynamicMethod("Poke", null, args, typeof(TOwn), true); + var il = mb.GetILGenerator(); + var minfo = propertyInfo.GetSetMethod(); + var opcode = (minfo.IsVirtual || minfo.IsAbstract) ? OpCodes.Callvirt : OpCodes.Call; + + il.Emit(OpCodes.Ldarg_1); // push arg1 + il.Emit(OpCodes.Ldarg_2); // push arg2 + il.Emit(opcode, minfo); // call [stack top-1].set_[propertyInfo]([stack top]) + il.Emit(OpCodes.Ret); // ret + return mb.CreateDelegate(typeof(Poke), null); + } } } diff --git a/src/Microsoft.ML.Api/ComponentCreation.cs b/src/Microsoft.ML.Api/ComponentCreation.cs index 3080a8197c..0a1e1cd605 100644 --- a/src/Microsoft.ML.Api/ComponentCreation.cs +++ b/src/Microsoft.ML.Api/ComponentCreation.cs @@ -6,6 +6,7 @@ using System.IO; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Model; namespace Microsoft.ML.Runtime.Api @@ -304,12 +305,20 @@ public static IDataScorerTransform CreateScorer(this IHostEnvironment env, strin env.CheckValue(predictor, nameof(predictor)); env.CheckValueOrNull(trainSchema); - var subComponent = SubComponent.Parse(settings); - var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor.Pred, subComponent); + ICommandLineComponentFactory scorerFactorySettings = ParseScorerSettings(settings); + var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor.Pred, scorerFactorySettings: scorerFactorySettings); var mapper = bindable.Bind(env, data.Schema); return CreateCore(env, settings, data.Data, mapper, trainSchema); } + private static ICommandLineComponentFactory ParseScorerSettings(string settings) + { + return CmdParser.CreateComponentFactory( + typeof(IComponentFactory), + typeof(SignatureDataScorer), + settings); + } + /// /// Creates a default data scorer appropriate to the predictor's prediction kind. /// diff --git a/src/Microsoft.ML.Api/DataViewConstructionUtils.cs b/src/Microsoft.ML.Api/DataViewConstructionUtils.cs index e940ea9d4d..c50e48e16f 100644 --- a/src/Microsoft.ML.Api/DataViewConstructionUtils.cs +++ b/src/Microsoft.ML.Api/DataViewConstructionUtils.cs @@ -118,7 +118,7 @@ private Delegate CreateGetter(int index) var colType = DataView.Schema.GetColumnType(index); var column = DataView._schema.SchemaDefn.Columns[index]; - var outputType = column.IsComputed ? column.ReturnType : column.FieldInfo.FieldType; + var outputType = column.OutputType; var genericType = outputType; Func del; diff --git a/src/Microsoft.ML.Api/InternalSchemaDefinition.cs b/src/Microsoft.ML.Api/InternalSchemaDefinition.cs index 3edf7599a4..4c20f25d62 100644 --- a/src/Microsoft.ML.Api/InternalSchemaDefinition.cs +++ b/src/Microsoft.ML.Api/InternalSchemaDefinition.cs @@ -23,21 +23,23 @@ internal sealed class InternalSchemaDefinition public class Column { public readonly string ColumnName; - public readonly FieldInfo FieldInfo; + public readonly MemberInfo MemberInfo; public readonly ParameterInfo ReturnParameterInfo; public readonly ColumnType ColumnType; public readonly bool IsComputed; public readonly Delegate Generator; private readonly Dictionary _metadata; public Dictionary Metadata { get { return _metadata; } } - public Type ReturnType {get { return ReturnParameterInfo.ParameterType.GetElementType(); }} + public Type ComputedReturnType {get { return ReturnParameterInfo.ParameterType.GetElementType(); }} + public Type FieldOrPropertyType => (MemberInfo is FieldInfo) ? (MemberInfo as FieldInfo).FieldType : (MemberInfo as PropertyInfo).PropertyType; + public Type OutputType => IsComputed ? ComputedReturnType : FieldOrPropertyType; - public Column(string columnName, ColumnType columnType, FieldInfo fieldInfo) : - this(columnName, columnType, fieldInfo, null, null) { } + public Column(string columnName, ColumnType columnType, MemberInfo memberInfo) : + this(columnName, columnType, memberInfo, null, null) { } - public Column(string columnName, ColumnType columnType, FieldInfo fieldInfo, + public Column(string columnName, ColumnType columnType, MemberInfo memberInfo, Dictionary metadataInfos) : - this(columnName, columnType, fieldInfo, null, metadataInfos) { } + this(columnName, columnType, memberInfo, null, metadataInfos) { } public Column(string columnName, ColumnType columnType, Delegate generator) : this(columnName, columnType, null, generator, null) { } @@ -46,7 +48,7 @@ public Column(string columnName, ColumnType columnType, Delegate generator, Dictionary metadataInfos) : this(columnName, columnType, null, generator, metadataInfos) { } - private Column(string columnName, ColumnType columnType, FieldInfo fieldInfo = null, + private Column(string columnName, ColumnType columnType, MemberInfo memberInfo = null, Delegate generator = null, Dictionary metadataInfos = null) { Contracts.AssertNonEmpty(columnName); @@ -55,8 +57,8 @@ private Column(string columnName, ColumnType columnType, FieldInfo fieldInfo = n if (generator == null) { - Contracts.AssertValue(fieldInfo); - FieldInfo = fieldInfo; + Contracts.AssertValue(memberInfo); + MemberInfo = memberInfo; } else { @@ -95,8 +97,8 @@ public void AssertRep() // If Column is computed type, it must have a generator. Contracts.Assert(IsComputed == (Generator != null)); - // Column must have either a generator or a fieldInfo value. - Contracts.Assert((Generator == null) != (FieldInfo == null)); + // Column must have either a generator or a memberInfo value. + Contracts.Assert((Generator == null) != (MemberInfo == null)); // Additional Checks if there is a generator. if (Generator == null) @@ -115,9 +117,7 @@ public void AssertRep() Contracts.Assert(Generator.GetMethodInfo().ReturnType == typeof(void)); // Checks that the return type of the generator is compatible with ColumnType. - bool isVector; - DataKind datakind; - GetVectorAndKind(ReturnType, "return type", out isVector, out datakind); + GetVectorAndKind(ComputedReturnType, "return type", out bool isVector, out DataKind datakind); Contracts.Assert(isVector == ColumnType.IsVector); Contracts.Assert(datakind == ColumnType.ItemType.RawKind); } @@ -131,19 +131,30 @@ private InternalSchemaDefinition(Column[] columns) } /// - /// Given a field info on a type, returns whether this appears to be a vector type, + /// Given a field or property info on a type, returns whether this appears to be a vector type, /// and also the associated data kind for this type. If a data kind could not /// be determined, this will throw. /// - /// The field info to inspect. + /// The field or property info to inspect. /// Whether this appears to be a vector type. /// The data kind of the type, or items of this type if vector. - public static void GetVectorAndKind(FieldInfo fieldInfo, out bool isVector, out DataKind kind) + public static void GetVectorAndKind(MemberInfo memberInfo, out bool isVector, out DataKind kind) { - Contracts.AssertValue(fieldInfo); - Type rawFieldType = fieldInfo.FieldType; - var name = fieldInfo.Name; - GetVectorAndKind(rawFieldType, name, out isVector, out kind); + Contracts.AssertValue(memberInfo); + switch (memberInfo) + { + case FieldInfo fieldInfo: + GetVectorAndKind(fieldInfo.FieldType, fieldInfo.Name, out isVector, out kind); + break; + + case PropertyInfo propertyInfo: + GetVectorAndKind(propertyInfo.PropertyType, propertyInfo.Name, out isVector, out kind); + break; + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + } } /// @@ -211,23 +222,27 @@ public static InternalSchemaDefinition Create(Type userType, SchemaDefinition us bool isVector; DataKind kind; - FieldInfo fieldInfo = null; + MemberInfo memberInfo = null; if (!col.IsComputed) { - fieldInfo = userType.GetField(col.MemberName); + memberInfo = userType.GetField(col.MemberName); + + if (memberInfo == null) + memberInfo = userType.GetProperty(col.MemberName); - if (fieldInfo == null) - throw Contracts.ExceptParam(nameof(userSchemaDefinition), "No field with name '{0}' found in type '{1}'", + if (memberInfo == null) + throw Contracts.ExceptParam(nameof(userSchemaDefinition), "No field or property with name '{0}' found in type '{1}'", col.MemberName, userType.FullName); //Clause to handle the field that may be used to expose the cursor channel. //This field does not need a column. - if (fieldInfo.FieldType == typeof(IChannel)) + if ( (memberInfo is FieldInfo && (memberInfo as FieldInfo).FieldType == typeof(IChannel)) || + (memberInfo is PropertyInfo && (memberInfo as PropertyInfo).PropertyType == typeof(IChannel))) continue; - GetVectorAndKind(fieldInfo, out isVector, out kind); + GetVectorAndKind(memberInfo, out isVector, out kind); } else { @@ -268,7 +283,7 @@ public static InternalSchemaDefinition Create(Type userType, SchemaDefinition us dstCols[i] = col.IsComputed ? new Column(colName, colType, col.Generator, col.Metadata) - : new Column(colName, colType, fieldInfo, col.Metadata); + : new Column(colName, colType, memberInfo, col.Metadata); } return new InternalSchemaDefinition(dstCols); diff --git a/src/Microsoft.ML.Api/SchemaDefinition.cs b/src/Microsoft.ML.Api/SchemaDefinition.cs index e08845a87e..3258df4ffd 100644 --- a/src/Microsoft.ML.Api/SchemaDefinition.cs +++ b/src/Microsoft.ML.Api/SchemaDefinition.cs @@ -14,7 +14,7 @@ namespace Microsoft.ML.Runtime.Api /// /// Attach to a member of a class to indicate that the item type should be of class key. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class KeyTypeAttribute : Attribute { // REVIEW: Property based, but should I just have a constructor? @@ -46,7 +46,7 @@ public KeyTypeAttribute() /// Allows a member to be marked as a vector valued field, primarily allowing one to set /// the dimensionality of the resulting array. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class VectorTypeAttribute : Attribute { private readonly int[] _dims; @@ -66,7 +66,7 @@ public VectorTypeAttribute(params int[] dims) /// Describes column information such as name and the source columns indicies that this /// column encapsulates. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class ColumnAttribute : Attribute { public ColumnAttribute(string ordinal, string name = null) @@ -97,7 +97,7 @@ public ColumnAttribute(string ordinal, string name = null) /// Allows a member to specify its column name directly, as opposed to the default /// behavior of using the member name as the column name. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class ColumnNameAttribute : Attribute { private readonly string _name; @@ -119,7 +119,7 @@ public ColumnNameAttribute(string name) /// /// Mark this member as not being exposed as a column in the schema. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class NoColumnAttribute : Attribute { } @@ -128,7 +128,7 @@ public sealed class NoColumnAttribute : Attribute /// Mark a member that implements exactly IChannel as being permitted to receive /// channel information from an external channel. /// - [AttributeUsage(AttributeTargets.Field, AllowMultiple = false, Inherited = true)] + [AttributeUsage(AttributeTargets.Field | AttributeTargets.Property, AllowMultiple = false, Inherited = true)] public sealed class CursorChannelAttribute : Attribute { /// @@ -158,19 +158,40 @@ public static bool TrySetCursorChannel(IExceptionContext ectx, T obj, IChanne .Where(x => x.GetCustomAttributes(typeof(CursorChannelAttribute), false).Any()) .ToArray(); + var cursorChannelAttrProperties = typeof(T) + .GetProperties(BindingFlags.Public | BindingFlags.Instance) + .Where(x => x.CanRead && x.CanWrite && x.GetGetMethod() != null && x.GetSetMethod() != null && x.GetIndexParameters().Length == 0) + .Where(x => x.GetCustomAttributes(typeof(CursorChannelAttribute), false).Any()); + + var cursorChannelAttrMembers = (cursorChannelAttrFields as IEnumerable).Concat(cursorChannelAttrProperties).ToArray(); + //Check that there is at most one such field. - if (cursorChannelAttrFields.Length == 0) + if (cursorChannelAttrMembers.Length == 0) return false; - ectx.Check(cursorChannelAttrFields.Length == 1, - "Only one field with CursorChannel attribute is allowed."); + ectx.Check(cursorChannelAttrMembers.Length == 1, + "Only one public field or property with CursorChannel attribute is allowed."); //Check that the marked field has type IChannel. - var cursorChannelFieldInfo = cursorChannelAttrFields[0]; - ectx.Check(cursorChannelFieldInfo.FieldType == typeof(IChannel), - "Field marked as CursorChannel must have type IChannel."); - - cursorChannelFieldInfo.SetValue(obj, channel); + var cursorChannelAttrMemberInfo = cursorChannelAttrMembers[0]; + switch (cursorChannelAttrMemberInfo) + { + case FieldInfo cursorChannelAttrFieldInfo: + ectx.Check(cursorChannelAttrFieldInfo.FieldType == typeof(IChannel), + "Field marked as CursorChannel must have type IChannel."); + cursorChannelAttrFieldInfo.SetValue(obj, channel); + break; + + case PropertyInfo cursorChannelAttrPropertyInfo: + ectx.Check(cursorChannelAttrPropertyInfo.PropertyType == typeof(IChannel), + "Property marked as CursorChannel must have type IChannel."); + cursorChannelAttrPropertyInfo.SetValue(obj, channel); + break; + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + } return true; } } @@ -319,37 +340,63 @@ public static SchemaDefinition Create(Type userType) SchemaDefinition cols = new SchemaDefinition(); HashSet colNames = new HashSet(); - foreach (var fieldInfo in userType.GetFields()) + + var fieldInfos = userType.GetFields(BindingFlags.Public | BindingFlags.Instance); + var propertyInfos = + userType + .GetProperties(BindingFlags.Public | BindingFlags.Instance) + .Where(x => x.CanRead && x.CanWrite && x.GetGetMethod() != null && x.GetSetMethod() != null && x.GetIndexParameters().Length == 0); + + var memberInfos = (fieldInfos as IEnumerable).Concat(propertyInfos).ToArray(); + + foreach (var memberInfo in memberInfos) { // Clause to handle the field that may be used to expose the cursor channel. // This field does not need a column. // REVIEW: maybe validate the channel attribute now, instead // of later at cursor creation. - if (fieldInfo.FieldType == typeof(IChannel)) - continue; - // Const fields do not need to be mapped. - if (fieldInfo.IsLiteral) - continue; + switch (memberInfo) + { + case FieldInfo fieldInfo: + if (fieldInfo.FieldType == typeof(IChannel)) + continue; + + // Const fields do not need to be mapped. + if (fieldInfo.IsLiteral) + continue; + + break; - if (fieldInfo.GetCustomAttribute() != null) + case PropertyInfo propertyInfo: + if (propertyInfo.PropertyType == typeof(IChannel)) + continue; + break; + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + } + + if (memberInfo.GetCustomAttribute() != null) continue; - var mappingAttr = fieldInfo.GetCustomAttribute(); - var mappingNameAttr = fieldInfo.GetCustomAttribute(); - string name = mappingAttr?.Name ?? mappingNameAttr?.Name ?? fieldInfo.Name; + + var mappingAttr = memberInfo.GetCustomAttribute(); + var mappingNameAttr = memberInfo.GetCustomAttribute(); + string name = mappingAttr?.Name ?? mappingNameAttr?.Name ?? memberInfo.Name; // Disallow duplicate names, because the field enumeration order is not actually // well defined, so we are not gauranteed to have consistent "hiding" from run to // run, across different .NET versions. if (!colNames.Add(name)) throw Contracts.ExceptParam(nameof(userType), "Duplicate column name '{0}' detected, this is disallowed", name); - InternalSchemaDefinition.GetVectorAndKind(fieldInfo, out bool isVector, out DataKind kind); + InternalSchemaDefinition.GetVectorAndKind(memberInfo, out bool isVector, out DataKind kind); PrimitiveType itemType; - var keyAttr = fieldInfo.GetCustomAttribute(); + var keyAttr = memberInfo.GetCustomAttribute(); if (keyAttr != null) { if (!KeyType.IsValidDataKind(kind)) - throw Contracts.ExceptParam(nameof(userType), "Member {0} marked with KeyType attribute, but does not appear to be a valid kind of data for a key type", fieldInfo.Name); + throw Contracts.ExceptParam(nameof(userType), "Member {0} marked with KeyType attribute, but does not appear to be a valid kind of data for a key type", memberInfo.Name); itemType = new KeyType(kind, keyAttr.Min, keyAttr.Count, keyAttr.Contiguous); } else @@ -357,9 +404,9 @@ public static SchemaDefinition Create(Type userType) // Get the column type. ColumnType columnType; - var vectorAttr = fieldInfo.GetCustomAttribute(); + var vectorAttr = memberInfo.GetCustomAttribute(); if (vectorAttr != null && !isVector) - throw Contracts.ExceptParam(nameof(userType), "Member {0} marked with VectorType attribute, but does not appear to be a vector type", fieldInfo.Name); + throw Contracts.ExceptParam(nameof(userType), "Member {0} marked with VectorType attribute, but does not appear to be a vector type", memberInfo.Name); if (isVector) { int[] dims = vectorAttr?.Dims; @@ -373,7 +420,7 @@ public static SchemaDefinition Create(Type userType) else columnType = itemType; - cols.Add(new Column() { MemberName = fieldInfo.Name, ColumnName = name, ColumnType = columnType }); + cols.Add(new Column() { MemberName = memberInfo.Name, ColumnName = name, ColumnType = columnType }); } return cols; } diff --git a/src/Microsoft.ML.Api/TypedCursor.cs b/src/Microsoft.ML.Api/TypedCursor.cs index cd8198e14d..19f9a7cf72 100644 --- a/src/Microsoft.ML.Api/TypedCursor.cs +++ b/src/Microsoft.ML.Api/TypedCursor.cs @@ -103,11 +103,11 @@ private TypedCursorable(IHostEnvironment env, IDataView data, bool ignoreMissing throw _host.Except("Column '{0}' not found in the data view", col.ColumnName); } var realColType = _data.Schema.GetColumnType(colIndex); - if (!IsCompatibleType(realColType, col.FieldInfo)) + if (!IsCompatibleType(realColType, col.MemberInfo)) { throw _host.Except( - "Can't bind the IDataView column '{0}' of type '{1}' to field '{2}' of type '{3}'.", - col.ColumnName, realColType, col.FieldInfo.Name, col.FieldInfo.FieldType.FullName); + "Can't bind the IDataView column '{0}' of type '{1}' to field or property '{2}' of type '{3}'.", + col.ColumnName, realColType, col.MemberInfo.Name, col.FieldOrPropertyType.FullName); } acceptedCols.Add(col); @@ -130,14 +130,12 @@ private TypedCursorable(IHostEnvironment env, IDataView data, bool ignoreMissing } /// - /// Returns whether the column type can be bound to field . + /// Returns whether the column type can be bound to field . /// They must both be vectors or scalars, and the raw data kind should match. /// - private static bool IsCompatibleType(ColumnType colType, FieldInfo fieldInfo) + private static bool IsCompatibleType(ColumnType colType, MemberInfo memberInfo) { - bool isVector; - DataKind kind; - InternalSchemaDefinition.GetVectorAndKind(fieldInfo, out isVector, out kind); + InternalSchemaDefinition.GetVectorAndKind(memberInfo, out bool isVector, out DataKind kind); if (isVector) return colType.IsVector && colType.ItemType.RawKind == kind; else @@ -269,8 +267,7 @@ public ValueGetter GetIdGetter() private Action GenerateSetter(IRow input, int index, InternalSchemaDefinition.Column column, Delegate poke, Delegate peek) { var colType = input.Schema.GetColumnType(index); - var fieldInfo = column.FieldInfo; - var fieldType = fieldInfo.FieldType; + var fieldType = column.OutputType; var genericType = fieldType; Func> del; if (fieldType.IsArray) @@ -431,7 +428,7 @@ private Action GenerateSetter(IRow input, int index, InternalSchemaDefinit else { // REVIEW: Is this even possible? - throw Ch.ExceptNotImpl("Type '{0}' is not yet supported.", fieldInfo.FieldType.FullName); + throw Ch.ExceptNotImpl("Type '{0}' is not yet supported.", column.OutputType.FullName); } MethodInfo meth = del.GetMethodInfo().GetGenericMethodDefinition().MakeGenericMethod(genericType); return (Action)meth.Invoke(this, new object[] { input, index, poke, peek }); diff --git a/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj index ae327a26c2..f9a1b5ef27 100644 --- a/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj +++ b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj @@ -15,6 +15,7 @@ + diff --git a/src/Microsoft.ML.Core/CommandLine/ArgumentAttribute.cs b/src/Microsoft.ML.Core/CommandLine/ArgumentAttribute.cs index 405e207773..64fe3b5b80 100644 --- a/src/Microsoft.ML.Core/CommandLine/ArgumentAttribute.cs +++ b/src/Microsoft.ML.Core/CommandLine/ArgumentAttribute.cs @@ -34,6 +34,7 @@ public enum VisibilityType private string _specialPurpose; private VisibilityType _visibility; private string _name; + private Type _signatureType; /// /// Allows control of command line parsing. @@ -139,5 +140,11 @@ public bool IsRequired { get { return ArgumentType.Required == (_type & ArgumentType.Required); } } + + public Type SignatureType + { + get { return _signatureType; } + set { _signatureType = value; } + } } } \ No newline at end of file diff --git a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs index eb85fcce12..bd37a96f7b 100644 --- a/src/Microsoft.ML.Core/CommandLine/CmdParser.cs +++ b/src/Microsoft.ML.Core/CommandLine/CmdParser.cs @@ -249,6 +249,18 @@ public enum SettingsFlags Default = ShortNames | NoSlashes } + /// + /// An IComponentFactory that is used in the command line. + /// + /// This allows components to be created by name, signature type, and a settings string. + /// + public interface ICommandLineComponentFactory : IComponentFactory + { + Type SignatureType { get; } + string Name { get; } + string GetSettingsString(); + } + /// /// Parser for command line arguments. /// @@ -797,7 +809,8 @@ private bool ParseArgumentList(ArgumentInfo info, string[] strs, object destinat ModuleCatalog.ComponentInfo component; if (IsCurlyGroup(value) && value.Length == 2) arg.Field.SetValue(destination, null); - else if (_catalog.Value.TryFindComponentCaseInsensitive(arg.Field.FieldType, value, out component)) + else if (!arg.IsCollection && + _catalog.Value.TryFindComponentCaseInsensitive(arg.Field.FieldType, value, out component)) { var activator = Activator.CreateInstance(component.ArgumentType); if (!IsCurlyGroup(value) && i + 1 < strs.Length && IsCurlyGroup(strs[i + 1])) @@ -810,8 +823,9 @@ private bool ParseArgumentList(ArgumentInfo info, string[] strs, object destinat } else { - Report("Error: Failed to find component with name '{0}' for option '{1}'", value, arg.LongName); - hadError |= true; + hadError |= !arg.SetValue(this, ref values[arg.Index], value, tag, destination); + if (!IsCurlyGroup(value) && i + 1 < strs.Length && IsCurlyGroup(strs[i + 1])) + hadError |= !arg.SetValue(this, ref values[arg.Index], strs[++i], "", destination); } continue; } @@ -1283,6 +1297,41 @@ private static bool IsValidItemType(Type type) typeBase.IsEnum; } + /// + /// Creates an ICommandLineComponentFactory given the factory type, signature type, + /// and a command line string. + /// + public static ICommandLineComponentFactory CreateComponentFactory( + Type factoryType, + Type signatureType, + string settings) + { + ParseComponentStrings(settings, out string name, out string args); + + string[] argsArray = string.IsNullOrEmpty(args) ? Array.Empty() : new string[] { args }; + + return ComponentFactoryFactory.CreateComponentFactory(factoryType, signatureType, name, argsArray); + } + + private static void ParseComponentStrings(string str, out string kind, out string args) + { + kind = args = null; + if (string.IsNullOrWhiteSpace(str)) + return; + str = str.Trim(); + int ich = str.IndexOf('{'); + if (ich < 0) + { + kind = str; + return; + } + if (ich == 0 || str[str.Length - 1] != '}') + throw Contracts.Except("Invalid Component string: mismatched braces, or empty component name."); + + kind = str.Substring(0, ich); + args = CmdLexer.UnquoteValue(str.Substring(ich)); + } + private sealed class ArgValue { public readonly string FirstValue; @@ -1532,6 +1581,8 @@ private sealed class Argument // Used for help and composing settings strings. public readonly object DefaultValue; + private readonly Type _signatureType; + // For custom types. private readonly ArgumentInfo _infoCustom; private readonly ConstructorInfo _ctorCustom; @@ -1559,6 +1610,7 @@ public Argument(int index, string name, string[] nicks, object defaults, Argumen IsDefault = attr is DefaultArgumentAttribute; Contracts.Assert(!IsDefault || Utils.Size(ShortNames) == 0); IsHidden = attr.Hide; + _signatureType = attr.SignatureType; if (field.FieldType.IsArray) { @@ -1664,6 +1716,48 @@ public bool Finish(CmdParser owner, ArgValue val, object destination) Field.SetValue(destination, com); } + else if (IsSingleComponentFactory) + { + bool haveName = false; + string name = null; + string[] settings = null; + for (int i = 0; i < Utils.Size(values);) + { + string str = (string)values[i].Value; + if (str.StartsWith("{")) + { + i++; + continue; + } + if (haveName) + { + owner.Report("Duplicate component kind for argument {0}", LongName); + error = true; + } + name = str; + haveName = true; + values.RemoveAt(i); + } + + if (Utils.Size(values) > 0) + settings = values.Select(x => (string)x.Value).ToArray(); + + Contracts.Check(_signatureType != null, "ComponentFactory Arguments need a SignatureType set."); + if (ComponentFactoryFactory.TryCreateComponentFactory( + ItemType, + _signatureType, + name, + settings, + out ICommandLineComponentFactory factory)) + { + Field.SetValue(destination, factory); + } + else + { + owner.Report("There was an error creating the ComponentFactory. Ensure '{0}' is configured correctly.", LongName); + error = true; + } + } else if (IsMultiSubComponent) { // REVIEW: the kind should not be separated from settings: everything related @@ -1711,6 +1805,79 @@ public bool Finish(CmdParser owner, ArgValue val, object destination) Field.SetValue(destination, arr); } } + else if (IsMultiComponentFactory) + { + // REVIEW: the kind should not be separated from settings: everything related + // to one item should go into one value, not multiple values + if (IsTaggedCollection) + { + // Tagged collection of IComponentFactory + var comList = new List>(); + + for (int i = 0; i < Utils.Size(values);) + { + string tag = values[i].Key; + string name = (string)values[i++].Value; + string[] settings = null; + if (i < values.Count && IsCurlyGroup((string)values[i].Value) && string.IsNullOrEmpty(values[i].Key)) + settings = new string[] { (string)values[i++].Value }; + if (ComponentFactoryFactory.TryCreateComponentFactory( + ItemValueType, + _signatureType, + name, + settings, + out ICommandLineComponentFactory factory)) + { + comList.Add(new KeyValuePair(tag, factory)); + } + else + { + owner.Report("There was an error creating the ComponentFactory. Ensure '{0}' is configured correctly.", LongName); + error = true; + } + } + + var arr = Array.CreateInstance(ItemType, comList.Count); + for (int i = 0; i < arr.Length; i++) + { + var kvp = Activator.CreateInstance(ItemType, comList[i].Key, comList[i].Value); + arr.SetValue(kvp, i); + } + + Field.SetValue(destination, arr); + } + else + { + // Collection of IComponentFactory + var comList = new List(); + for (int i = 0; i < Utils.Size(values);) + { + string name = (string)values[i++].Value; + string[] settings = null; + if (i < values.Count && IsCurlyGroup((string)values[i].Value)) + settings = new string[] { (string)values[i++].Value }; + if (ComponentFactoryFactory.TryCreateComponentFactory( + ItemValueType, + _signatureType, + name, + settings, + out ICommandLineComponentFactory factory)) + { + comList.Add(factory); + } + else + { + owner.Report("There was an error creating the ComponentFactory. Ensure '{0}' is configured correctly.", LongName); + error = true; + } + } + + var arr = Array.CreateInstance(ItemValueType, comList.Count); + for (int i = 0; i < arr.Length; i++) + arr.SetValue(comList[i], i); + Field.SetValue(destination, arr); + } + } else if (IsTaggedCollection) { var res = Array.CreateInstance(ItemType, Utils.Size(values)); @@ -1784,7 +1951,7 @@ public bool SetValue(CmdParser owner, ref ArgValue val, string value, string tag } val.Values.Add(new KeyValuePair(tag, newValue)); } - else if (IsSingleSubComponent) + else if (IsSingleSubComponent || IsComponentFactory) { Contracts.Assert(newValue is string || newValue == null); Contracts.Assert((string)newValue != ""); @@ -1834,7 +2001,7 @@ private bool ParseValue(CmdParser owner, string data, out object value) return false; } - if (IsSubComponentItemType) + if (IsSubComponentItemType || IsComponentFactory) { value = data; return true; @@ -2186,19 +2353,28 @@ private string GetString(IExceptionContext ectx, object value, StringBuilder buf string name; var catalog = ModuleCatalog.CreateInstance(ectx); var type = value.GetType(); - bool success = catalog.TryGetComponentShortName(type, out name); - Contracts.Assert(success); - - var settings = GetSettings(ectx, value, Activator.CreateInstance(type)); - buffer.Clear(); - buffer.Append(name); - if (!string.IsNullOrWhiteSpace(settings)) + bool isModuleComponent = catalog.TryGetComponentShortName(type, out name); + if (isModuleComponent) { - StringBuilder sb = new StringBuilder(); - CmdQuoter.QuoteValue(settings, sb, true); - buffer.Append(sb); + var settings = GetSettings(ectx, value, Activator.CreateInstance(type)); + buffer.Clear(); + buffer.Append(name); + if (!string.IsNullOrWhiteSpace(settings)) + { + StringBuilder sb = new StringBuilder(); + CmdQuoter.QuoteValue(settings, sb, true); + buffer.Append(sb); + } + return buffer.ToString(); + } + else if (value is ICommandLineComponentFactory) + { + return value.ToString(); + } + else + { + throw ectx.Except($"IComponentFactory instances either need to be EntryPointComponents or implement {nameof(ICommandLineComponentFactory)}."); } - return buffer.ToString(); } return value.ToString(); @@ -2344,9 +2520,191 @@ public bool IsMultiSubComponent { get { return IsSubComponentItemType && Field.FieldType.IsArray; } } + public bool IsSingleComponentFactory + { + get { return IsComponentFactory && !Field.FieldType.IsArray; } + } + + public bool IsMultiComponentFactory + { + get { return IsComponentFactory && Field.FieldType.IsArray; } + } + public bool IsCustomItemType { get { return _infoCustom != null; } } } + + /// + /// A factory class for creating IComponentFactory instances. + /// + private static class ComponentFactoryFactory + { + public static ICommandLineComponentFactory CreateComponentFactory( + Type factoryType, + Type signatureType, + string name, + string[] settings) + { + if (!TryCreateComponentFactory(factoryType, signatureType, name, settings, out ICommandLineComponentFactory factory)) + { + throw Contracts.ExceptNotImpl("ComponentFactoryFactory can only create IComponentFactory<> types with 4 or less type args."); + } + + return factory; + } + + public static bool TryCreateComponentFactory( + Type factoryType, + Type signatureType, + string name, + string[] settings, + out ICommandLineComponentFactory factory) + { + + if (factoryType == null || + !typeof(IComponentFactory).IsAssignableFrom(factoryType) || + !factoryType.IsGenericType) + { + factory = null; + return false; + } + + Type componentFactoryType; + switch (factoryType.GenericTypeArguments.Length) + { + case 1: componentFactoryType = typeof(ComponentFactory<>); break; + case 2: componentFactoryType = typeof(ComponentFactory<,>); break; + case 3: componentFactoryType = typeof(ComponentFactory<,,>); break; + case 4: componentFactoryType = typeof(ComponentFactory<,,,>); break; + default: + factory = null; + return false; + } + + factory = (ICommandLineComponentFactory)Activator.CreateInstance( + componentFactoryType.MakeGenericType(factoryType.GenericTypeArguments), + signatureType, + name, + settings); + return true; + } + + private abstract class ComponentFactory : ICommandLineComponentFactory + { + public Type SignatureType { get; } + public string Name { get; } + private string[] Settings { get; } + + protected ComponentFactory(Type signatureType, string name, string[] settings) + { + SignatureType = signatureType; + Name = name; + + if (settings == null || (settings.Length == 1 && string.IsNullOrEmpty(settings[0]))) + { + settings = Array.Empty(); + } + Settings = settings; + } + + public string GetSettingsString() + { + return CombineSettings(Settings); + } + + public override string ToString() + { + if (string.IsNullOrEmpty(Name) && Settings.Length == 0) + return "{}"; + + if (Settings.Length == 0) + return Name; + + string str = CombineSettings(Settings); + StringBuilder sb = new StringBuilder(); + CmdQuoter.QuoteValue(str, sb, true); + return Name + sb.ToString(); + } + } + + private class ComponentFactory : ComponentFactory, IComponentFactory + where TComponent : class + { + public ComponentFactory(Type signatureType, string name, string[] settings) + : base(signatureType, name, settings) + { + } + + public TComponent CreateComponent(IHostEnvironment env) + { + return ComponentCatalog.CreateInstance( + env, + SignatureType, + Name, + GetSettingsString()); + } + } + + private class ComponentFactory : ComponentFactory, IComponentFactory + where TComponent : class + { + public ComponentFactory(Type signatureType, string name, string[] settings) + : base(signatureType, name, settings) + { + } + + public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1) + { + return ComponentCatalog.CreateInstance( + env, + SignatureType, + Name, + GetSettingsString(), + argument1); + } + } + + private class ComponentFactory : ComponentFactory, IComponentFactory + where TComponent : class + { + public ComponentFactory(Type signatureType, string name, string[] settings) + : base(signatureType, name, settings) + { + } + + public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2) + { + return ComponentCatalog.CreateInstance( + env, + SignatureType, + Name, + GetSettingsString(), + argument1, + argument2); + } + } + + private class ComponentFactory : ComponentFactory, IComponentFactory + where TComponent : class + { + public ComponentFactory(Type signatureType, string name, string[] settings) + : base(signatureType, name, settings) + { + } + + public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2, TArg3 argument3) + { + return ComponentCatalog.CreateInstance( + env, + SignatureType, + Name, + GetSettingsString(), + argument1, + argument2, + argument3); + } + } + } } } \ No newline at end of file diff --git a/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs b/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs index 3b56e8bb36..ddbbd2a500 100644 --- a/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs +++ b/src/Microsoft.ML.Core/ComponentModel/ComponentCatalog.cs @@ -343,7 +343,7 @@ private static bool ShouldSkipPath(string path) case "libvw.dll": case "matrixinterf.dll": case "Microsoft.ML.neuralnetworks.gpucuda.dll": - case "Microsoft.ML.mklimports.dll": + case "MklImports.dll": case "microsoft.research.controls.decisiontrees.dll": case "Microsoft.ML.neuralnetworks.sse.dll": case "neuraltreeevaluator.dll": @@ -832,10 +832,15 @@ public static LoadableClassInfo[] FindLoadableClasses() public static LoadableClassInfo GetLoadableClassInfo(string loadName) { - Contracts.CheckParam(typeof(TSig).BaseType == typeof(MulticastDelegate), nameof(TSig), "TSig must be a delegate type"); + return GetLoadableClassInfo(loadName, typeof(TSig)); + } + + public static LoadableClassInfo GetLoadableClassInfo(string loadName, Type signatureType) + { + Contracts.CheckParam(signatureType.BaseType == typeof(MulticastDelegate), nameof(signatureType), "signatureType must be a delegate type"); Contracts.CheckValueOrNull(loadName); loadName = (loadName ?? "").ToLowerInvariant().Trim(); - return FindClassCore(new LoadableClassInfo.Key(loadName, typeof(TSig))); + return FindClassCore(new LoadableClassInfo.Key(loadName, signatureType)); } public static LoadableClassInfo GetLoadableClassInfo(SubComponent sub) @@ -886,6 +891,18 @@ public static TRes CreateInstance(this SubComponent comp throw Contracts.Except("Unknown loadable class: {0}", comp.Kind).MarkSensitive(MessageSensitivity.None); } + /// + /// Create an instance of the indicated component with the given extra parameters. + /// + public static TRes CreateInstance(IHostEnvironment env, Type signatureType, string name, string options, params object[] extra) + where TRes : class + { + TRes result; + if (TryCreateInstance(env, signatureType, out result, name, options, extra)) + return result; + throw Contracts.Except("Unknown loadable class: {0}", name).MarkSensitive(MessageSensitivity.None); + } + /// /// Try to create an instance of the indicated component with the given extra parameters. If there is no /// such component in the catalog, returns false. Any other error results in an exception. @@ -913,13 +930,19 @@ public static bool TryCreateInstance(IHostEnvironment env, out TRes /// public static bool TryCreateInstance(IHostEnvironment env, out TRes result, string name, string options, params object[] extra) where TRes : class + { + return TryCreateInstance(env, typeof(TSig), out result, name, options, extra); + } + + private static bool TryCreateInstance(IHostEnvironment env, Type signatureType, out TRes result, string name, string options, params object[] extra) + where TRes : class { Contracts.CheckValue(env, nameof(env)); - env.Check(typeof(TSig).BaseType == typeof(MulticastDelegate)); + env.Check(signatureType.BaseType == typeof(MulticastDelegate)); env.CheckValueOrNull(name); string nameLower = (name ?? "").ToLowerInvariant().Trim(); - LoadableClassInfo info = FindClassCore(new LoadableClassInfo.Key(nameLower, typeof(TSig))); + LoadableClassInfo info = FindClassCore(new LoadableClassInfo.Key(nameLower, signatureType)); if (info == null) { result = null; diff --git a/src/Microsoft.ML.Core/Data/ColumnType.cs b/src/Microsoft.ML.Core/Data/ColumnType.cs index 0cff911e77..96764d68f1 100644 --- a/src/Microsoft.ML.Core/Data/ColumnType.cs +++ b/src/Microsoft.ML.Core/Data/ColumnType.cs @@ -242,6 +242,10 @@ public bool IsStandardScalar /// internal virtual int ValueCountCore { get { return 1; } } + // IEquatable interface recommends also to override base class implementations of + // Object.Equals(Object) and GetHashCode. In classes below where Equals(ColumnType other) + // is effectively a referencial comparison, there is no need to override base class implementations + // of Object.Equals(Object) (and GetHashCode) since its also a referencial comparison. public abstract bool Equals(ColumnType other); /// @@ -789,6 +793,16 @@ public override bool Equals(ColumnType other) return true; } + public override bool Equals(object other) + { + return other is ColumnType tmp && Equals(tmp); + } + + public override int GetHashCode() + { + return Hashing.CombinedHash(RawKind.GetHashCode(), _contiguous, _min, _count); + } + public override string ToString() { if (_count > 0) @@ -940,6 +954,21 @@ public override bool Equals(ColumnType other) return true; } + public override bool Equals(object other) + { + return other is ColumnType tmp && Equals(tmp); + } + + public override int GetHashCode() + { + int hash = Hashing.CombinedHash(_itemType.GetHashCode(), _size); + int count = Utils.Size(_dims); + hash = Hashing.CombineHash(hash, count.GetHashCode()); + for (int i = 0; i < count; i++) + hash = Hashing.CombineHash(hash, _dims[i].GetHashCode()); + return hash; + } + /// /// Returns true if current has the same item type of other, and the size /// of other is unknown or the current size is equal to the size of other. diff --git a/src/Microsoft.ML.Core/EntryPoints/ComponentFactory.cs b/src/Microsoft.ML.Core/EntryPoints/ComponentFactory.cs index 9334f0f225..1f8d5b0e8f 100644 --- a/src/Microsoft.ML.Core/EntryPoints/ComponentFactory.cs +++ b/src/Microsoft.ML.Core/EntryPoints/ComponentFactory.cs @@ -24,7 +24,7 @@ public interface IArgsComponent : IComponentFactory /// /// An interface for creating a component with no extra parameters (other than an ). /// - public interface IComponentFactory: IComponentFactory + public interface IComponentFactory : IComponentFactory { TComponent CreateComponent(IHostEnvironment env); } @@ -44,4 +44,104 @@ public interface IComponentFactory : ICompon { TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2); } + + /// + /// An interface for creating a component when we take three extra parameters (and an ). + /// + public interface IComponentFactory : IComponentFactory + { + TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2, TArg3 argument3); + } + + /// + /// A utility class for creating instances. + /// + public static class ComponentFactoryUtils + { + /// + /// Creates a component factory with no extra parameters (other than an ) + /// that simply wraps a delegate which creates the component. + /// + public static IComponentFactory CreateFromFunction(Func factory) + { + return new SimpleComponentFactory(factory); + } + + /// + /// Creates a component factory when we take one extra parameter (and an + /// ) that simply wraps a delegate which creates the component. + /// + public static IComponentFactory CreateFromFunction(Func factory) + { + return new SimpleComponentFactory(factory); + } + + /// + /// Creates a component factory when we take three extra parameters (and an + /// ) that simply wraps a delegate which creates the component. + /// + public static IComponentFactory CreateFromFunction(Func factory) + { + return new SimpleComponentFactory(factory); + } + + /// + /// A class for creating a component with no extra parameters (other than an ) + /// that simply wraps a delegate which creates the component. + /// + private sealed class SimpleComponentFactory : IComponentFactory + { + private readonly Func _factory; + + public SimpleComponentFactory(Func factory) + { + _factory = factory; + } + + public TComponent CreateComponent(IHostEnvironment env) + { + return _factory(env); + } + } + + /// + /// A class for creating a component when we take one extra parameter + /// (and an ) that simply wraps a delegate which + /// creates the component. + /// + private sealed class SimpleComponentFactory : IComponentFactory + { + private readonly Func _factory; + + public SimpleComponentFactory(Func factory) + { + _factory = factory; + } + + public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1) + { + return _factory(env, argument1); + } + } + + /// + /// A class for creating a component when we take three extra parameters + /// (and an ) that simply wraps a delegate which + /// creates the component. + /// + private sealed class SimpleComponentFactory : IComponentFactory + { + private readonly Func _factory; + + public SimpleComponentFactory(Func factory) + { + _factory = factory; + } + + public TComponent CreateComponent(IHostEnvironment env, TArg1 argument1, TArg2 argument2, TArg3 argument3) + { + return _factory(env, argument1, argument2, argument3); + } + } + } } diff --git a/src/Microsoft.ML.CpuMath/AlignedMatrix.cs b/src/Microsoft.ML.CpuMath/AlignedMatrix.cs index 67f05ee7cf..f76ec7815d 100644 --- a/src/Microsoft.ML.CpuMath/AlignedMatrix.cs +++ b/src/Microsoft.ML.CpuMath/AlignedMatrix.cs @@ -550,7 +550,7 @@ public void ZeroItems(int[] indices) // REVIEW: Ideally, we'd adjust the indices once so we wouldn't need to // repeatedly deal with padding adjustments. - SseUtils.ZeroMatrixItems(Items, ColCount, ColCountPhy, indices); + CpuMathUtils.ZeroMatrixItems(Items, ColCount, ColCountPhy, indices); } } diff --git a/src/Microsoft.ML.CpuMath/CpuAligenedMathUtils.cs b/src/Microsoft.ML.CpuMath/CpuAligenedMathUtils.cs index ad53810ff3..9c7fa5ae1f 100644 --- a/src/Microsoft.ML.CpuMath/CpuAligenedMathUtils.cs +++ b/src/Microsoft.ML.CpuMath/CpuAligenedMathUtils.cs @@ -16,7 +16,7 @@ public static void AssertCompatible(ICpuFullMatrix values) #if DEBUG var mat = values as TMatrix; Contracts.AssertValue(mat); - Contracts.Assert(mat.Items.CbAlign == SseUtils.CbAlign); + Contracts.Assert(mat.Items.CbAlign == CpuMathUtils.Vector128Alignment); #endif } @@ -29,7 +29,7 @@ public static void AssertCompatible(ICpuVector values) #if DEBUG CpuAlignedVector vec = values as CpuAlignedVector; Contracts.AssertValue(vec); - Contracts.Assert(vec.Items.CbAlign == SseUtils.CbAlign); + Contracts.Assert(vec.Items.CbAlign == CpuMathUtils.Vector128Alignment); #endif } @@ -89,7 +89,7 @@ public static void MatTimesSrc(bool add, ICpuFullMatrix mat, ICpuVector src, ICp bool colMajor = typeof(TMatrix) == typeof(CpuAlignedMatrixCol); AssertCompatible(mat, src, dst); var m = A(mat); - SseUtils.MatTimesSrc(colMajor, add, m.Items, A(src).Items, A(dst).Items, m.RunCnt); + CpuMathUtils.MatTimesSrc(colMajor, add, m.Items, A(src).Items, A(dst).Items, m.RunCnt); } /// @@ -108,7 +108,7 @@ public static void MatTranTimesSrc(bool add, ICpuFullMatrix mat, ICpuVector src, bool colMajor = typeof(TMatrix) == typeof(CpuAlignedMatrixCol); AssertCompatible(mat, dst, src); var m = A(mat); - SseUtils.MatTimesSrc(!colMajor, add, m.Items, A(src).Items, A(dst).Items, m.RunCnt); + CpuMathUtils.MatTimesSrc(!colMajor, add, m.Items, A(src).Items, A(dst).Items, m.RunCnt); } } diff --git a/src/Microsoft.ML.CpuMath/CpuMathUtils.netcoreapp.cs b/src/Microsoft.ML.CpuMath/CpuMathUtils.netcoreapp.cs new file mode 100644 index 0000000000..81d7acf25a --- /dev/null +++ b/src/Microsoft.ML.CpuMath/CpuMathUtils.netcoreapp.cs @@ -0,0 +1,962 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Runtime.Intrinsics.X86; +using System; + +namespace Microsoft.ML.Runtime.Internal.CpuMath +{ + public static partial class CpuMathUtils + { + // The count of bytes in Vector128, corresponding to _cbAlign in AlignedArray + public const int Vector128Alignment = 16; + + public static void MatTimesSrc(bool tran, bool add, AlignedArray mat, AlignedArray src, AlignedArray dst, int crun) + { + Contracts.Assert(mat.Size == dst.Size * src.Size); + Contracts.Assert(crun >= 0); + + if (Sse.IsSupported) + { + if (!tran) + { + Contracts.Assert(crun <= dst.Size); + SseIntrinsics.MatMulA(add, mat, src, dst, crun, src.Size); + } + else + { + Contracts.Assert(crun <= src.Size); + SseIntrinsics.MatMulTranA(add, mat, src, dst, dst.Size, crun); + } + } + else + { + if (!tran) + { + Contracts.Assert(crun <= dst.Size); + for (int i = 0; i < crun; i++) + { + float dotProduct = 0; + for (int j = 0; j < src.Size; j++) + { + dotProduct += mat[i * src.Size + j] * src[j]; + } + + if (add) + { + dst[i] += dotProduct; + } + else + { + dst[i] = dotProduct; + } + } + } + else + { + Contracts.Assert(crun <= src.Size); + for (int i = 0; i < dst.Size; i++) + { + float dotProduct = 0; + for (int j = 0; j < crun; j++) + { + dotProduct += mat[j * src.Size + i] * src[j]; + } + + if (add) + { + dst[i] += dotProduct; + } + else + { + dst[i] = dotProduct; + } + } + } + } + } + + public static void MatTimesSrc(bool tran, bool add, AlignedArray mat, int[] rgposSrc, AlignedArray srcValues, + int posMin, int iposMin, int iposLim, AlignedArray dst, int crun) + { + Contracts.AssertValue(rgposSrc); + Contracts.Assert(iposMin >= 0); + Contracts.Assert(iposMin <= iposLim); + Contracts.Assert(iposLim <= rgposSrc.Length); + Contracts.Assert(mat.Size == dst.Size * srcValues.Size); + + if (iposMin >= iposLim) + { + if (!add) + dst.ZeroItems(); + return; + } + + Contracts.AssertNonEmpty(rgposSrc); + Contracts.Assert(crun >= 0); + + if (Sse.IsSupported) + { + if (!tran) + { + Contracts.Assert(crun <= dst.Size); + SseIntrinsics.MatMulPA(add, mat, rgposSrc, srcValues, posMin, iposMin, iposLim, dst, crun, srcValues.Size); + } + else + { + Contracts.Assert(crun <= srcValues.Size); + SseIntrinsics.MatMulTranPA(add, mat, rgposSrc, srcValues, posMin, iposMin, iposLim, dst, dst.Size); + } + } + else + { + if (!tran) + { + Contracts.Assert(crun <= dst.Size); + for (int i = 0; i < crun; i++) + { + float dotProduct = 0; + for (int j = iposMin; j < iposLim; j++) + { + int col = rgposSrc[j] - posMin; + dotProduct += mat[i * srcValues.Size + col] * srcValues[col]; + } + + if (add) + { + dst[i] += dotProduct; + } + else + { + dst[i] = dotProduct; + } + } + } + else + { + Contracts.Assert(crun <= srcValues.Size); + for (int i = 0; i < dst.Size; i++) + { + float dotProduct = 0; + for (int j = iposMin; j < iposLim; j++) + { + int col = rgposSrc[j] - posMin; + dotProduct += mat[col * dst.Size + i] * srcValues[col]; + } + + if (add) + { + dst[i] += dotProduct; + } + else + { + dst[i] = dotProduct; + } + } + + } + } + } + + public static void Add(float a, float[] dst, int count) + { + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= dst.Length); + + Add(a, new Span(dst, 0, count)); + } + + private static void Add(float a, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddScalarU(a, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] += a; + } + } + } + + public static void Scale(float a, float[] dst, int count) + { + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= dst.Length); + + Scale(a, new Span(dst, 0, count)); + } + + public static void Scale(float a, float[] dst, int offset, int count) + { + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset < (dst.Length - count)); + + Scale(a, new Span(dst, offset, count)); + } + + private static void Scale(float a, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.ScaleU(a, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] *= a; + } + } + } + + // dst = a * src + public static void Scale(float a, float[] src, float[] dst, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= dst.Length); + + Scale(a, new Span(src, 0, count), new Span(dst, 0, count)); + } + + private static void Scale(float a, Span src, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.ScaleSrcU(a, src, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] = a * src[i]; + } + } + } + + // dst[i] = a * (dst[i] + b) + public static void ScaleAdd(float a, float b, float[] dst, int count) + { + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= dst.Length); + + ScaleAdd(a, b, new Span(dst, 0, count)); + } + + private static void ScaleAdd(float a, float b, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.ScaleAddU(a, b, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] = a * (dst[i] + b); + } + } + } + + public static void AddScale(float a, float[] src, float[] dst, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= dst.Length); + + AddScale(a, new Span(src, 0, count), new Span(dst, 0, count)); + } + + public static void AddScale(float a, float[] src, float[] dst, int dstOffset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(dstOffset >= 0); + Contracts.Assert(dstOffset < dst.Length); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= (dst.Length - dstOffset)); + + AddScale(a, new Span(src, 0, count), new Span(dst, dstOffset, count)); + } + + private static void AddScale(float a, Span src, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddScaleU(a, src, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] += a * src[i]; + } + } + } + + public static void AddScale(float a, float[] src, int[] indices, float[] dst, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(indices); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(count < dst.Length); + + AddScale(a, new Span(src), new Span(indices, 0, count), new Span(dst)); + } + + public static void AddScale(float a, float[] src, int[] indices, float[] dst, int dstOffset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(indices); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(dstOffset >= 0); + Contracts.Assert(dstOffset < dst.Length); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(count < (dst.Length - dstOffset)); + + AddScale(a, new Span(src), new Span(indices, 0, count), + new Span(dst, dstOffset, dst.Length - dstOffset)); + } + + private static void AddScale(float a, Span src, Span indices, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddScaleSU(a, src, indices, dst); + } + else + { + for (int i = 0; i < indices.Length; i++) + { + int index = indices[i]; + dst[index] += a * src[i]; + } + } + } + + public static void AddScaleCopy(float a, float[] src, float[] dst, float[] res, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(dst); + Contracts.AssertNonEmpty(res); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= dst.Length); + Contracts.Assert(count <= res.Length); + + AddScaleCopy(a, new Span(src, 0, count), new Span(dst, 0, count), new Span(res, 0, count)); + } + + private static void AddScaleCopy(float a, Span src, Span dst, Span res) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddScaleCopyU(a, src, dst, res); + } + else + { + for (int i = 0; i < res.Length; i++) + { + res[i] = a * src[i] + dst[i]; + } + } + } + + public static void Add(float[] src, float[] dst, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= dst.Length); + + Add(new Span(src, 0, count), new Span(dst, 0, count)); + } + + private static void Add(Span src, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddU(src, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] += src[i]; + } + } + } + + public static void Add(float[] src, int[] indices, float[] dst, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(indices); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(count < dst.Length); + + Add(new Span(src), new Span(indices, 0, count), new Span(dst)); + } + + public static void Add(float[] src, int[] indices, float[] dst, int dstOffset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(indices); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(dstOffset >= 0); + Contracts.Assert(dstOffset < dst.Length); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(count <= (dst.Length - dstOffset)); + + Add(new Span(src), new Span(indices, 0, count), + new Span(dst, dstOffset, dst.Length - dstOffset)); + } + + private static void Add(Span src, Span indices, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.AddSU(src, indices, dst); + } + else + { + for (int i = 0; i < indices.Length; i++) + { + int index = indices[i]; + dst[index] += src[i]; + } + } + } + + public static void MulElementWise(float[] src1, float[] src2, float[] dst, int count) + { + Contracts.AssertNonEmpty(src1); + Contracts.AssertNonEmpty(src2); + Contracts.AssertNonEmpty(dst); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src1.Length); + Contracts.Assert(count <= src2.Length); + + MulElementWise(new Span(src1, 0, count), new Span(src2, 0, count), + new Span(dst, 0, count)); + } + + private static void MulElementWise(Span src1, Span src2, Span dst) + { + if (Sse.IsSupported) + { + SseIntrinsics.MulElementWiseU(src1, src2, dst); + } + else + { + for (int i = 0; i < dst.Length; i++) + { + dst[i] = src1[i] * src2[i]; + } + } + } + + public static float Sum(float[] src, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + + return Sum(new Span(src, 0, count)); + } + + public static float Sum(float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return Sum(new Span(src, offset, count)); + } + + private static float Sum(Span src) + { + if (Sse.IsSupported) + { + return SseIntrinsics.SumU(src); + } + else + { + float sum = 0; + for (int i = 0; i < src.Length; i++) + { + sum += src[i]; + } + return sum; + } + } + + public static float SumSq(float[] src, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + + return SumSq(new Span(src, 0, count)); + } + + public static float SumSq(float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return SumSq(new Span(src, offset, count)); + } + + private static float SumSq(Span src) + { + if (Sse.IsSupported) + { + return SseIntrinsics.SumSqU(src); + } + else + { + float result = 0; + for (int i = 0; i < src.Length; i++) + { + result += src[i] * src[i]; + } + return result; + } + } + + public static float SumSq(float mean, float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return SumSq(mean, new Span(src, offset, count)); + } + + private static float SumSq(float mean, Span src) + { + if (Sse.IsSupported) + { + return (mean == 0) ? SseIntrinsics.SumSqU(src) : SseIntrinsics.SumSqDiffU(mean, src); + } + else + { + float result = 0; + for (int i = 0; i < src.Length; i++) + { + result += (src[i] - mean) * (src[i] - mean); + } + return result; + } + } + + public static float SumAbs(float[] src, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + + return SumAbs(new Span(src, 0, count)); + } + + public static float SumAbs(float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return SumAbs(new Span(src, offset, count)); + } + + private static float SumAbs(Span src) + { + if (Sse.IsSupported) + { + return SseIntrinsics.SumAbsU(src); + } + else + { + float sum = 0; + for (int i = 0; i < src.Length; i++) + { + sum += Math.Abs(src[i]); + } + return sum; + } + } + + public static float SumAbs(float mean, float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return SumAbs(mean, new Span(src, offset, count)); + } + + private static float SumAbs(float mean, Span src) + { + if (Sse.IsSupported) + { + return (mean == 0) ? SseIntrinsics.SumAbsU(src) : SseIntrinsics.SumAbsDiffU(mean, src); + } + else + { + float sum = 0; + for (int i = 0; i < src.Length; i++) + { + sum += Math.Abs(src[i] - mean); + } + return sum; + } + } + + public static float MaxAbs(float[] src, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + + return MaxAbs(new Span(src, 0, count)); + } + + public static float MaxAbs(float[] src, int offset, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (src.Length - count)); + + return MaxAbs(new Span(src, offset, count)); + } + + private static float MaxAbs(Span src) + { + if (Sse.IsSupported) + { + return SseIntrinsics.MaxAbsU(src); + } + else + { + float max = 0; + for (int i = 0; i < src.Length; i++) + { + float abs = Math.Abs(src[i]); + if (abs > max) + { + max = abs; + } + } + return max; + } + } + + public static float MaxAbsDiff(float mean, float[] src, int count) + { + Contracts.AssertNonEmpty(src); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + + return MaxAbsDiff(mean, new Span(src, 0, count)); + } + + private static float MaxAbsDiff(float mean, Span src) + { + if (Sse.IsSupported) + { + return SseIntrinsics.MaxAbsDiffU(mean, src); + } + else + { + float max = 0; + for (int i = 0; i < src.Length; i++) + { + float abs = Math.Abs(src[i] - mean); + if (abs > max) + { + max = abs; + } + } + return max; + } + } + + public static float DotProductDense(float[] a, float[] b, int count) + { + Contracts.AssertNonEmpty(a); + Contracts.AssertNonEmpty(b); + Contracts.Assert(count > 0); + Contracts.Assert(a.Length >= count); + Contracts.Assert(b.Length >= count); + + return DotProductDense(new Span(a, 0, count), new Span(b, 0, count)); + } + + public static float DotProductDense(float[] a, int offset, float[] b, int count) + { + Contracts.AssertNonEmpty(a); + Contracts.AssertNonEmpty(b); + Contracts.Assert(count > 0); + Contracts.Assert(count <= b.Length); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset <= (a.Length - count)); + + return DotProductDense(new Span(a, offset, count), new Span(b, 0, count)); + } + + private static float DotProductDense(Span a, Span b) + { + if (Sse.IsSupported) + { + return SseIntrinsics.DotU(a, b); + } + else + { + float result = 0; + for (int i = 0; i < b.Length; i++) + { + result += a[i] * b[i]; + } + return result; + } + } + + public static float DotProductSparse(float[] a, float[] b, int[] indices, int count) + { + Contracts.AssertNonEmpty(a); + Contracts.AssertNonEmpty(b); + Contracts.AssertNonEmpty(indices); + Contracts.Assert(count > 0); + Contracts.Assert(count < a.Length); + Contracts.Assert(count <= b.Length); + Contracts.Assert(count <= indices.Length); + + return DotProductSparse(new Span(a), new Span(b), + new Span(indices, 0, count)); + } + + public static float DotProductSparse(float[] a, int offset, float[] b, int[] indices, int count) + { + Contracts.AssertNonEmpty(a); + Contracts.AssertNonEmpty(b); + Contracts.AssertNonEmpty(indices); + Contracts.Assert(count > 0); + Contracts.Assert(count < (a.Length - offset)); + Contracts.Assert(count <= b.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(offset >= 0); + Contracts.Assert(offset < a.Length); + + return DotProductSparse(new Span(a, offset, a.Length - offset), + new Span(b), new Span(indices, 0, count)); + } + + private static float DotProductSparse(Span a, Span b, Span indices) + { + if (Sse.IsSupported) + { + return SseIntrinsics.DotSU(a, b, indices); + } + else + { + float result = 0; + for (int i = 0; i < indices.Length; i++) + { + int index = indices[i]; + result += a[index] * b[i]; + } + return result; + } + } + + public static float L2DistSquared(float[] a, float[] b, int count) + { + Contracts.AssertNonEmpty(a); + Contracts.AssertNonEmpty(b); + Contracts.Assert(count > 0); + Contracts.Assert(count <= a.Length); + Contracts.Assert(count <= b.Length); + + return L2DistSquared(new Span(a, 0, count), new Span(b, 0, count)); + } + + private static float L2DistSquared(Span a, Span b) + { + if (Sse.IsSupported) + { + return SseIntrinsics.Dist2(a, b); + } + else + { + float norm = 0; + for (int i = 0; i < b.Length; i++) + { + float distance = a[i] - b[i]; + norm += distance * distance; + } + return norm; + } + } + + public static void ZeroMatrixItems(AlignedArray dst, int ccol, int cfltRow, int[] indices) + { + Contracts.Assert(ccol > 0); + Contracts.Assert(ccol <= cfltRow); + + if (ccol == cfltRow) + { + ZeroItemsU(dst, dst.Size, indices, indices.Length); + } + else + { + ZeroMatrixItemsCore(dst, dst.Size, ccol, cfltRow, indices, indices.Length); + } + } + + private static unsafe void ZeroItemsU(AlignedArray dst, int c, int[] indices, int cindices) + { + fixed (float* pdst = &dst.Items[0]) + fixed (int* pidx = &indices[0]) + { + for (int i = 0; i < cindices; ++i) + { + int index = pidx[i]; + Contracts.Assert(index >= 0); + Contracts.Assert(index < c); + pdst[index] = 0; + } + } + } + + private static unsafe void ZeroMatrixItemsCore(AlignedArray dst, int c, int ccol, int cfltRow, int[] indices, int cindices) + { + fixed (float* pdst = &dst.Items[0]) + fixed (int* pidx = &indices[0]) + { + int ivLogMin = 0; + int ivLogLim = ccol; + int ivPhyMin = 0; + + for (int i = 0; i < cindices; ++i) + { + int index = pidx[i]; + Contracts.Assert(index >= 0); + Contracts.Assert(index < c); + + int col = index - ivLogMin; + if ((uint)col >= (uint)ccol) + { + Contracts.Assert(ivLogMin > index || index >= ivLogLim); + + int row = index / ccol; + ivLogMin = row * ccol; + ivLogLim = ivLogMin + ccol; + ivPhyMin = row * cfltRow; + + Contracts.Assert(index >= ivLogMin); + Contracts.Assert(index < ivLogLim); + col = index - ivLogMin; + } + + pdst[ivPhyMin + col] = 0; + } + } + } + + public static void SdcaL1UpdateDense(float primalUpdate, int length, float[] src, float threshold, float[] v, float[] w) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(v); + Contracts.AssertNonEmpty(w); + Contracts.Assert(length > 0); + Contracts.Assert(length <= src.Length); + Contracts.Assert(length <= v.Length); + Contracts.Assert(length <= w.Length); + + SdcaL1UpdateDense(primalUpdate, new Span(src, 0, length), threshold, new Span(v, 0, length), new Span(w, 0, length)); + } + + private static void SdcaL1UpdateDense(float primalUpdate, Span src, float threshold, Span v, Span w) + { + if (Sse.IsSupported) + { + SseIntrinsics.SdcaL1UpdateU(primalUpdate, src, threshold, v, w); + } + else + { + for (int i = 0; i < src.Length; i++) + { + v[i] += src[i] * primalUpdate; + float value = v[i]; + w[i] = Math.Abs(value) > threshold ? (value > 0 ? value - threshold : value + threshold) : 0; + } + } + } + + // REVIEW NEEDED: The second argument "length" is unused even in the existing code. + public static void SdcaL1UpdateSparse(float primalUpdate, int length, float[] src, int[] indices, int count, float threshold, float[] v, float[] w) + { + Contracts.AssertNonEmpty(src); + Contracts.AssertNonEmpty(indices); + Contracts.AssertNonEmpty(v); + Contracts.AssertNonEmpty(w); + Contracts.Assert(count > 0); + Contracts.Assert(count <= src.Length); + Contracts.Assert(count <= indices.Length); + Contracts.Assert(count < length); + Contracts.Assert(length <= v.Length); + Contracts.Assert(length <= w.Length); + + SdcaL1UpdateSparse(primalUpdate, new Span(src, 0, count), new Span(indices, 0, count), threshold, new Span(v), new Span(w)); + } + + private static void SdcaL1UpdateSparse(float primalUpdate, Span src, Span indices, float threshold, Span v, Span w) + { + if (Sse.IsSupported) + { + SseIntrinsics.SdcaL1UpdateSU(primalUpdate, src, indices, threshold, v, w); + } + else + { + for (int i = 0; i < indices.Length; i++) + { + int index = indices[i]; + v[index] += src[i] * primalUpdate; + float value = v[index]; + w[index] = Math.Abs(value) > threshold ? (value > 0 ? value - threshold : value + threshold) : 0; + } + } + } + } +} diff --git a/src/Microsoft.ML.CpuMath/CpuMathUtils.netstandard.cs b/src/Microsoft.ML.CpuMath/CpuMathUtils.netstandard.cs new file mode 100644 index 0000000000..6f480b0f25 --- /dev/null +++ b/src/Microsoft.ML.CpuMath/CpuMathUtils.netstandard.cs @@ -0,0 +1,85 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +namespace Microsoft.ML.Runtime.Internal.CpuMath +{ + public static partial class CpuMathUtils + { + // The count of bytes in Vector128, corresponding to _cbAlign in AlignedArray + public const int Vector128Alignment = 16; + + public static void MatTimesSrc(bool tran, bool add, AlignedArray mat, AlignedArray src, AlignedArray dst, int crun) => SseUtils.MatTimesSrc(tran, add, mat, src, dst, crun); + + public static void MatTimesSrc(bool tran, bool add, AlignedArray mat, int[] rgposSrc, AlignedArray srcValues, + int posMin, int iposMin, int iposLim, AlignedArray dst, int crun) => SseUtils.MatTimesSrc(tran, add, mat, rgposSrc, srcValues, posMin, iposMin, iposLim, dst, crun); + + public static void Add(float a, float[] dst, int count) => SseUtils.Add(a, dst, count); + + public static void Scale(float a, float[] dst, int count) => SseUtils.Scale(a, dst, count); + + public static void Scale(float a, float[] dst, int offset, int count) => SseUtils.Scale(a, dst, offset, count); + + public static void Scale(float a, float[] src, float[] dst, int count) => SseUtils.Scale(a, src, dst, count); + + public static void ScaleAdd(float a, float b, float[] dst, int count) => SseUtils.ScaleAdd(a, b, dst, count); + + public static void AddScale(float a, float[] src, float[] dst, int count) => SseUtils.AddScale(a, src, dst, count); + + public static void AddScale(float a, float[] src, float[] dst, int dstOffset, int count) => SseUtils.AddScale(a, src, dst, dstOffset, count); + + public static void AddScale(float a, float[] src, int[] indices, float[] dst, int count) => SseUtils.AddScale(a, src, indices, dst, count); + + public static void AddScale(float a, float[] src, int[] indices, float[] dst, int dstOffset, int count) => SseUtils.AddScale(a, src, indices, dst, dstOffset, count); + + public static void AddScaleCopy(float a, float[] src, float[] dst, float[] res, int count) => SseUtils.AddScaleCopy(a, src, dst, res, count); + + public static void Add(float[] src, float[] dst, int count) => SseUtils.Add(src, dst, count); + + public static void Add(float[] src, int[] indices, float[] dst, int count) => SseUtils.Add(src, indices, dst, count); + + public static void Add(float[] src, int[] indices, float[] dst, int dstOffset, int count) => SseUtils.Add(src, indices, dst, dstOffset, count); + + public static void MulElementWise(float[] src1, float[] src2, float[] dst, int count) => SseUtils.MulElementWise(src1, src2, dst, count); + + public static float Sum(float[] src, int count) => SseUtils.Sum(src, count); + + public static float Sum(float[] src, int offset, int count) => SseUtils.Sum(src, offset, count); + + public static float SumSq(float[] src, int count) => SseUtils.SumSq(src, count); + + public static float SumSq(float[] src, int offset, int count) => SseUtils.SumSq(src, offset, count); + + public static float SumSq(float mean, float[] src, int offset, int count) => SseUtils.SumSq(mean, src, offset, count); + + public static float SumAbs(float[] src, int count) => SseUtils.SumAbs(src, count); + + public static float SumAbs(float[] src, int offset, int count) => SseUtils.SumAbs(src, offset, count); + + public static float SumAbs(float mean, float[] src, int offset, int count) => SseUtils.SumAbs(mean, src, offset, count); + + public static float MaxAbs(float[] src, int count) => SseUtils.MaxAbs(src, count); + + public static float MaxAbs(float[] src, int offset, int count) => SseUtils.MaxAbs(src, offset, count); + + public static float MaxAbsDiff(float mean, float[] src, int count) => SseUtils.MaxAbsDiff(mean, src, count); + + public static float DotProductDense(float[] a, float[] b, int count) => SseUtils.DotProductDense(a, b, count); + + public static float DotProductDense(float[] a, int offset, float[] b, int count) => SseUtils.DotProductDense(a, offset, b, count); + + public static float DotProductSparse(float[] a, float[] b, int[] indices, int count) => SseUtils.DotProductSparse(a, b, indices, count); + + public static float DotProductSparse(float[] a, int offset, float[] b, int[] indices, int count) => SseUtils.DotProductSparse(a, offset, b, indices, count); + + public static float L2DistSquared(float[] a, float[] b, int count) => SseUtils.L2DistSquared(a, b, count); + + public static void ZeroMatrixItems(AlignedArray dst, int ccol, int cfltRow, int[] indices) => SseUtils.ZeroMatrixItems(dst, ccol, cfltRow, indices); + + public static void SdcaL1UpdateDense(float primalUpdate, int length, float[] src, float threshold, float[] v, float[] w) + => SseUtils.SdcaL1UpdateDense(primalUpdate, length, src, threshold, v, w); + + public static void SdcaL1UpdateSparse(float primalUpdate, int length, float[] src, int[] indices, int count, float threshold, float[] v, float[] w) + => SseUtils.SdcaL1UpdateSparse(primalUpdate, length, src, indices, count, threshold, v, w); + } +} diff --git a/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj b/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj index bde7ae89f5..b6c95b93f4 100644 --- a/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj +++ b/src/Microsoft.ML.CpuMath/Microsoft.ML.CpuMath.csproj @@ -2,21 +2,29 @@ Debug;Release;Debug-Intrinsics;Release-Intrinsics - $(Configuration.EndsWith('-Intrinsics')) - netstandard2.0 netstandard2.0;netcoreapp3.0 Microsoft.ML.CpuMath true $(DefineConstants);CORECLR;PRIVATE_CONTRACTS + 7.3 + + + + - + + + + + + \ No newline at end of file diff --git a/src/Microsoft.ML.CpuMath/Sse.cs b/src/Microsoft.ML.CpuMath/Sse.cs index 68e6ee906b..13de22dd5b 100644 --- a/src/Microsoft.ML.CpuMath/Sse.cs +++ b/src/Microsoft.ML.CpuMath/Sse.cs @@ -2,8 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System; - namespace Microsoft.ML.Runtime.Internal.CpuMath { /// diff --git a/src/Microsoft.ML.CpuMath/SseIntrinsics.cs b/src/Microsoft.ML.CpuMath/SseIntrinsics.cs new file mode 100644 index 0000000000..bf7ad03e34 --- /dev/null +++ b/src/Microsoft.ML.CpuMath/SseIntrinsics.cs @@ -0,0 +1,1189 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +// The exported function names need to be unique (can't be disambiguated based on signature), hence +// we introduce suffix letters to indicate the general patterns used. +// * A suffix means aligned and padded for SSE operations. +// * U suffix means unaligned and unpadded. +// * S suffix means sparse (unaligned) vector. +// * P suffix means sparse (unaligned) partial vector - the vector is only part of a larger sparse vector. +// * R suffix means sparse matrix. +// * C suffix means convolution matrix. +// * D suffix means convolution matrix, with implicit source padding. +// * Tran means the matrix is transposed. + +using System; +using System.Runtime.CompilerServices; +using System.Runtime.Intrinsics; +using System.Runtime.Intrinsics.X86; + +namespace Microsoft.ML.Runtime.Internal.CpuMath +{ + internal static class SseIntrinsics + { + // The count of bytes in Vector128, corresponding to _cbAlign in AlignedArray + private const int Vector128Alignment = 16; + + private static bool Compat(AlignedArray a) + { + Contracts.AssertValue(a); + Contracts.Assert(a.Size > 0); + return a.CbAlign == Vector128Alignment; + } + + private static unsafe float* Ptr(AlignedArray a, float* p) + { + Contracts.AssertValue(a); + float* q = p + a.GetBase((long)p); + Contracts.Assert(((long)q & (Vector128Alignment - 1)) == 0); + return q; + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static unsafe Vector128 Load1(float* src, int* idx) + { + return Sse.SetScalarVector128(src[idx[0]]); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static unsafe Vector128 Load4(float* src, int* idx) + { + return Sse.SetVector128(src[idx[3]], src[idx[2]], src[idx[1]], src[idx[0]]); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static Vector128 Rotate(in Vector128 x) + { + // The control byte shuffles the four 32-bit floats of x: ABCD -> BCDA. + return Sse.Shuffle(x, x, 0x39); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static unsafe void Store4(in Vector128 x, float* dst, int* idx) + { + Sse.StoreScalar(dst + idx[0], x); + Vector128 rotated = Rotate(in x); + Sse.StoreScalar(dst + idx[1], rotated); + rotated = Rotate(in rotated); + Sse.StoreScalar(dst + idx[2], rotated); + rotated = Rotate(in rotated); + Sse.StoreScalar(dst + idx[3], rotated); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static Vector128 VectorSum(in Vector128 vector) + { + if (Sse3.IsSupported) + { + Vector128 partialSum = Sse3.HorizontalAdd(vector, vector); + return Sse3.HorizontalAdd(partialSum, partialSum); + } + else + { + Vector128 partialSum = Sse.Add(vector, Sse.MoveHighToLow(vector, vector)); + // The control byte shuffles the four 32-bit floats of partialSum: ABCD -> BADC. + return Sse.Add(partialSum, Sse.Shuffle(partialSum, partialSum, 0xB1)); + } + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static Vector128 VectorMax(in Vector128 vector) + { + Vector128 x1 = Sse.Shuffle(vector, vector, 0xB1); + Vector128 partialMax = Sse.Max(vector, x1); + x1 = Sse.Shuffle(partialMax, partialMax, 0x02); + return Sse.MaxScalar(partialMax, x1); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static Vector128 GetAbsMask() + { + return Sse2.IsSupported ? + Sse.StaticCast(Sse2.SetAllVector128(0x7FFFFFFF)) : + Sse.SetAllVector128(BitConverter.Int32BitsToSingle(0x7FFFFFFF)); + } + + [MethodImplAttribute(MethodImplOptions.AggressiveInlining)] + private static Vector128 GetNewDst(in Vector128 xDst1, in Vector128 signMask, in Vector128 xThreshold) + { + Vector128 xSign = Sse.And(xDst1, signMask); // result = 0x8000 0000 if xDst1 is negative or 0x0000 0000 otherwise + Vector128 xDst1Abs = Sse.Xor(xDst1, xSign); + Vector128 xCond = Sse.CompareGreaterThan(xDst1Abs, xThreshold); // result = 0xFFFF FFFF if true + Vector128 x2 = Sse.Xor(xSign, xThreshold); // -xThreshold if xDst1 is negative and +xThreshold otherwise + return Sse.And(Sse.Subtract(xDst1, x2), xCond); + } + + // Multiply matrix times vector into vector. + internal static unsafe void MatMulA(bool add, AlignedArray mat, AlignedArray src, AlignedArray dst, int crow, int ccol) + { + Contracts.Assert(Compat(mat)); + Contracts.Assert(Compat(src)); + Contracts.Assert(Compat(dst)); + + fixed (float* pSrcStart = &src.Items[0]) + fixed (float* pDstStart = &dst.Items[0]) + fixed (float* pMatStart = &mat.Items[0]) + { + float* psrc = Ptr(src, pSrcStart); + float* pdst = Ptr(dst, pDstStart); + float* pmat = Ptr(mat, pMatStart); + + float* pSrcEnd = psrc + ccol; + float* pDstEnd = pdst + crow; + float* pDstCurrent = pdst; + float* pMatCurrent = pmat; + + while (pDstCurrent < pDstEnd) + { + Vector128 res0 = Sse.SetZeroVector128(); + Vector128 res1 = res0; + Vector128 res2 = res0; + Vector128 res3 = res0; + + float* pSrcCurrent = psrc; + + while (pSrcCurrent < pSrcEnd) + { + float* pMatTemp = pMatCurrent; + + Vector128 x01 = Sse.LoadAlignedVector128(pMatTemp); + Vector128 x11 = Sse.LoadAlignedVector128(pMatTemp += ccol); + Vector128 x21 = Sse.LoadAlignedVector128(pMatTemp += ccol); + Vector128 x31 = Sse.LoadAlignedVector128(pMatTemp += ccol); + Vector128 x02 = Sse.LoadAlignedVector128(pSrcCurrent); + + res0 = Sse.Add(res0, Sse.Multiply(x01, x02)); + res1 = Sse.Add(res1, Sse.Multiply(x11, x02)); + res2 = Sse.Add(res2, Sse.Multiply(x21, x02)); + res3 = Sse.Add(res3, Sse.Multiply(x31, x02)); + + pSrcCurrent += 4; + pMatCurrent += 4; + } + + // Add up the entries of each, with the 4 results in res0 + res0 = Sse3.HorizontalAdd(res0, res1); + res2 = Sse3.HorizontalAdd(res2, res3); + res0 = Sse3.HorizontalAdd(res0, res2); + + if (add) + { + res0 = Sse.Add(res0, Sse.LoadAlignedVector128(pDstCurrent)); + } + Sse.StoreAligned(pDstCurrent, res0); + + pDstCurrent += 4; + pMatCurrent += 3 * ccol; + } + } + } + + // Partial sparse source vector. + internal static unsafe void MatMulPA(bool add, AlignedArray mat, int[] rgposSrc, AlignedArray src, + int posMin, int iposMin, int iposEnd, AlignedArray dst, int crow, int ccol) + { + Contracts.Assert(Compat(mat)); + Contracts.Assert(Compat(src)); + Contracts.Assert(Compat(dst)); + + // REVIEW: For extremely sparse inputs, interchanging the loops would + // likely be more efficient. + fixed (float* pSrcStart = &src.Items[0]) + fixed (float* pDstStart = &dst.Items[0]) + fixed (float* pMatStart = &mat.Items[0]) + fixed (int* pposSrc = &rgposSrc[0]) + { + float* psrc = Ptr(src, pSrcStart); + float* pdst = Ptr(dst, pDstStart); + float* pmat = Ptr(mat, pMatStart); + + int* pposMin = pposSrc + iposMin; + int* pposEnd = pposSrc + iposEnd; + float* pDstEnd = pdst + crow; + float* pm0 = pmat - posMin; + float* pSrcCurrent = psrc - posMin; + float* pDstCurrent = pdst; + + while (pDstCurrent < pDstEnd) + { + float* pm1 = pm0 + ccol; + float* pm2 = pm1 + ccol; + float* pm3 = pm2 + ccol; + Vector128 result = Sse.SetZeroVector128(); + + int* ppos = pposMin; + + while (ppos < pposEnd) + { + int col = *ppos; + Vector128 x1 = Sse.SetVector128(pm3[col], pm2[col], pm1[col], pm0[col]); + Vector128 x2 = Sse.SetAllVector128(pSrcCurrent[col]); + x2 = Sse.Multiply(x2, x1); + result = Sse.Add(result, x2); + + ppos++; + } + + if (add) + { + result = Sse.Add(result, Sse.LoadAlignedVector128(pDstCurrent)); + } + Sse.StoreAligned(pDstCurrent, result); + + pDstCurrent += 4; + pm0 += 4 * ccol; + } + } + } + + internal static unsafe void MatMulTranA(bool add, AlignedArray mat, AlignedArray src, AlignedArray dst, int crow, int ccol) + { + Contracts.Assert(Compat(mat)); + Contracts.Assert(Compat(src)); + Contracts.Assert(Compat(dst)); + + fixed (float* pSrcStart = &src.Items[0]) + fixed (float* pDstStart = &dst.Items[0]) + fixed (float* pMatStart = &mat.Items[0]) + { + float* psrc = Ptr(src, pSrcStart); + float* pdst = Ptr(dst, pDstStart); + float* pmat = Ptr(mat, pMatStart); + + float* pSrcEnd = psrc + ccol; + float* pDstEnd = pdst + crow; + float* pSrcCurrent = psrc; + float* pMatCurrent = pmat; + + if (!add) + { + Vector128 x01 = Sse.LoadAlignedVector128(pSrcCurrent); + // Replicate each 32-bit slot of x01 (ABCD) into its own register. + Vector128 x11 = Sse.Shuffle(x01, x01, 0x55); // B + Vector128 x21 = Sse.Shuffle(x01, x01, 0xAA); // C + Vector128 x31 = Sse.Shuffle(x01, x01, 0xFF); // D + x01 = Sse.Shuffle(x01, x01, 0x00); // A + + pSrcCurrent += 4; + + float* pDstCurrent = pdst; + + while (pDstCurrent < pDstEnd) + { + float* pMatTemp = pMatCurrent; + Vector128 x02 = Sse.LoadAlignedVector128(pMatTemp); + Vector128 x12 = Sse.LoadAlignedVector128(pMatTemp += crow); + Vector128 x22 = Sse.LoadAlignedVector128(pMatTemp += crow); + Vector128 x32 = Sse.LoadAlignedVector128(pMatTemp += crow); + + x02 = Sse.Multiply(x01, x02); + x12 = Sse.Multiply(x11, x12); + x22 = Sse.Multiply(x21, x22); + x32 = Sse.Multiply(x31, x32); + + x02 = Sse.Add(x02, x12); + x22 = Sse.Add(x22, x32); + x02 = Sse.Add(x02, x22); + + Sse.StoreAligned(pDstCurrent, x02); + + pDstCurrent += 4; + pMatCurrent += 4; + } + + pMatCurrent += 3 * crow; + } + + while (pSrcCurrent < pSrcEnd) + { + Vector128 x01 = Sse.LoadAlignedVector128(pSrcCurrent); + // Replicate each 32-bit slot of x01 (ABCD) into its own register. + Vector128 x11 = Sse.Shuffle(x01, x01, 0x55); // B + Vector128 x21 = Sse.Shuffle(x01, x01, 0xAA); // C + Vector128 x31 = Sse.Shuffle(x01, x01, 0xFF); // D + x01 = Sse.Shuffle(x01, x01, 0x00); // A + + float* pDstCurrent = pdst; + + while (pDstCurrent < pDstEnd) + { + float* pMatTemp = pMatCurrent; + + Vector128 x02 = Sse.LoadAlignedVector128(pMatTemp); + Vector128 x12 = Sse.LoadAlignedVector128(pMatTemp += crow); + Vector128 x22 = Sse.LoadAlignedVector128(pMatTemp += crow); + Vector128 x32 = Sse.LoadAlignedVector128(pMatTemp += crow); + Vector128 x3 = Sse.LoadAlignedVector128(pDstCurrent); + + x02 = Sse.Multiply(x01, x02); + x12 = Sse.Multiply(x11, x12); + x22 = Sse.Multiply(x21, x22); + x32 = Sse.Multiply(x31, x32); + + x02 = Sse.Add(x02, x12); + x22 = Sse.Add(x22, x32); + x02 = Sse.Add(x02, x22); + x3 = Sse.Add(x02, x3); + + Sse.StoreAligned(pDstCurrent, x3); + + pDstCurrent += 4; + pMatCurrent += 4; + } + + pMatCurrent += 3 * crow; + pSrcCurrent += 4; + } + } + } + + // Partial sparse source vector. + internal static unsafe void MatMulTranPA(bool add, AlignedArray mat, int[] rgposSrc, AlignedArray src, + int posMin, int iposMin, int iposEnd, AlignedArray dst, int crow) + { + Contracts.Assert(Compat(mat)); + Contracts.Assert(Compat(src)); + Contracts.Assert(Compat(dst)); + + fixed (float* pSrcStart = &src.Items[0]) + fixed (float* pDstStart = &dst.Items[0]) + fixed (float* pMatStart = &mat.Items[0]) + fixed (int* pposSrc = &rgposSrc[0]) + { + float* psrc = Ptr(src, pSrcStart); + float* pdst = Ptr(dst, pDstStart); + float* pmat = Ptr(mat, pMatStart); + + int* ppos = pposSrc + iposMin; + int* pposEnd = pposSrc + iposEnd; + float* pDstEnd = pdst + crow; + + if (!add) + { + int col = *ppos - posMin; + ppos++; + + Vector128 x0 = Sse.SetAllVector128(psrc[col]); + float* pDstCurrent = pdst; + float* pMatCurrent = pmat + col * crow; + + while (pDstCurrent < pDstEnd) + { + Vector128 x1 = Sse.LoadAlignedVector128(pMatCurrent); + x1 = Sse.Multiply(x1, x0); + Sse.StoreAligned(pDstCurrent, x1); + + pDstCurrent += 4; + pMatCurrent += 4; + } + } + + // REVIEW: Should we explore unrolling the outer loop? + while (ppos < pposEnd) + { + int col = *ppos - posMin; + + Vector128 x0 = Sse.SetAllVector128(psrc[col]); + float* pDstCurrent = pdst; + float* pMatCurrent = pmat + col * crow; + + while (pDstCurrent < pDstEnd) + { + Vector128 x1 = Sse.LoadAlignedVector128(pMatCurrent); + Vector128 x2 = Sse.LoadAlignedVector128(pDstCurrent); + x1 = Sse.Multiply(x1, x0); + x2 = Sse.Add(x2, x1); + Sse.StoreAligned(pDstCurrent, x2); + + pDstCurrent += 4; + pMatCurrent += 4; + } + + ppos++; + } + } + } + + // dst[i] += scale + internal static unsafe void AddScalarU(float scale, Span dst) + { + fixed (float* pdst = dst) + { + float* pDstEnd = pdst + dst.Length; + float* pDstCurrent = pdst; + + Vector128 x1 = Sse.SetAllVector128(scale); + + while (pDstCurrent + 4 <= pDstEnd) + { + Vector128 x2 = Sse.LoadVector128(pDstCurrent); + x2 = Sse.Add(x2, x1); + Sse.Store(pDstCurrent, x2); + + pDstCurrent += 4; + } + + while (pDstCurrent < pDstEnd) + { + Vector128 x2 = Sse.LoadScalarVector128(pDstCurrent); + x2 = Sse.AddScalar(x2, x1); + Sse.StoreScalar(pDstCurrent, x2); + + pDstCurrent++; + } + } + } + + internal static unsafe void ScaleU(float scale, Span dst) + { + Vector128 scaleVector = Sse.SetAllVector128(scale); + + fixed (float* pdst = dst) + { + float* pDstCurrent = pdst; + float* pEnd = pdst + dst.Length; + + while (pDstCurrent + 4 <= pEnd) + { + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + + dstVector = Sse.Multiply(scaleVector, dstVector); + Sse.Store(pDstCurrent, dstVector); + + pDstCurrent += 4; + } + + while (pDstCurrent < pEnd) + { + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + + dstVector = Sse.MultiplyScalar(scaleVector, dstVector); + Sse.StoreScalar(pDstCurrent, dstVector); + + pDstCurrent++; + } + } + } + + internal static unsafe void ScaleSrcU(float scale, Span src, Span dst) + { + Vector128 scaleVector = Sse.SetAllVector128(scale); + + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + float* pDstEnd = pdst + dst.Length; + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + + while (pDstCurrent + 4 <= pDstEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + srcVector = Sse.Multiply(srcVector, scaleVector); + Sse.Store(pDstCurrent, srcVector); + + pSrcCurrent += 4; + pDstCurrent += 4; + } + + while (pDstCurrent < pDstEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + srcVector = Sse.MultiplyScalar(srcVector, scaleVector); + Sse.StoreScalar(pDstCurrent, srcVector); + + pSrcCurrent++; + pDstCurrent++; + } + } + } + + // dst[i] = a * (dst[i] + b) + internal static unsafe void ScaleAddU(float a, float b, Span dst) + { + Vector128 x1 = Sse.SetAllVector128(a); + Vector128 x2 = Sse.SetAllVector128(b); + + fixed (float* pdst = dst) + { + float* pDstEnd = pdst + dst.Length; + float* pDstCurrent = pdst; + + while (pDstCurrent + 4 <= pDstEnd) + { + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + dstVector = Sse.Add(dstVector, x2); + dstVector = Sse.Multiply(dstVector, x1); + Sse.Store(pDstCurrent, dstVector); + + pDstCurrent += 4; + } + + while (pDstCurrent < pDstEnd) + { + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + dstVector = Sse.AddScalar(dstVector, x2); + dstVector = Sse.MultiplyScalar(dstVector, x1); + Sse.StoreScalar(pDstCurrent, dstVector); + + pDstCurrent++; + } + } + } + + internal static unsafe void AddScaleU(float scale, Span src, Span dst) + { + Vector128 scaleVector = Sse.SetAllVector128(scale); + + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + float* pEnd = pdst + dst.Length; + + while (pDstCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + + srcVector = Sse.Multiply(srcVector, scaleVector); + dstVector = Sse.Add(dstVector, srcVector); + Sse.Store(pDstCurrent, dstVector); + + pDstCurrent += 4; + pSrcCurrent += 4; + } + + while (pDstCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + + srcVector = Sse.MultiplyScalar(srcVector, scaleVector); + dstVector = Sse.AddScalar(dstVector, srcVector); + Sse.StoreScalar(pDstCurrent, dstVector); + + pDstCurrent++; + pSrcCurrent++; + } + } + } + + internal static unsafe void AddScaleCopyU(float scale, Span src, Span dst, Span result) + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (float* pres = result) + { + float* pResEnd = pres + result.Length; + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + float* pResCurrent = pres; + + Vector128 x1 = Sse.SetAllVector128(scale); + + while (pResCurrent + 4 <= pResEnd) + { + Vector128 x2 = Sse.LoadVector128(pSrcCurrent); + Vector128 x3 = Sse.LoadVector128(pDstCurrent); + x2 = Sse.Multiply(x2, x1); + x3 = Sse.Add(x3, x2); + Sse.Store(pResCurrent, x3); + + pSrcCurrent += 4; + pDstCurrent += 4; + pResCurrent += 4; + } + + while (pResCurrent < pResEnd) + { + Vector128 x2 = Sse.LoadScalarVector128(pSrcCurrent); + Vector128 x3 = Sse.LoadScalarVector128(pDstCurrent); + x2 = Sse.MultiplyScalar(x2, x1); + x3 = Sse.AddScalar(x3, x2); + Sse.StoreScalar(pResCurrent, x3); + + pSrcCurrent++; + pDstCurrent++; + pResCurrent++; + } + } + } + + internal static unsafe void AddScaleSU(float scale, Span src, Span idx, Span dst) + { + Vector128 scaleVector = Sse.SetAllVector128(scale); + + fixed (float* psrc = src) + fixed (int* pidx = idx) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + int* pIdxCurrent = pidx; + float* pDstCurrent = pdst; + int* pEnd = pidx + idx.Length; + + while (pIdxCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + Vector128 dstVector = Load4(pDstCurrent, pIdxCurrent); + + srcVector = Sse.Multiply(srcVector, scaleVector); + dstVector = Sse.Add(dstVector, srcVector); + Store4(in dstVector, pDstCurrent, pIdxCurrent); + + pIdxCurrent += 4; + pSrcCurrent += 4; + } + + while (pIdxCurrent < pEnd) + { + pDstCurrent[*pIdxCurrent] += scale * (*pSrcCurrent); + + pIdxCurrent++; + pSrcCurrent++; + } + } + } + + internal static unsafe void AddU(Span src, Span dst) + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + + Vector128 result = Sse.Add(srcVector, dstVector); + Sse.Store(pDstCurrent, result); + + pSrcCurrent += 4; + pDstCurrent += 4; + } + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + + Vector128 result = Sse.AddScalar(srcVector, dstVector); + Sse.StoreScalar(pDstCurrent, result); + + pSrcCurrent++; + pDstCurrent++; + } + } + } + + internal static unsafe void AddSU(Span src, Span idx, Span dst) + { + fixed (float* psrc = src) + fixed (int* pidx = idx) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + int* pIdxCurrent = pidx; + float* pDstCurrent = pdst; + int* pEnd = pidx + idx.Length; + + while (pIdxCurrent + 4 <= pEnd) + { + Vector128 srcVector = Load4(pDstCurrent, pIdxCurrent); + Vector128 dstVector = Sse.LoadVector128(pSrcCurrent); + + srcVector = Sse.Add(srcVector, dstVector); + Store4(in srcVector, pDstCurrent, pIdxCurrent); + + pIdxCurrent += 4; + pSrcCurrent += 4; + } + + while (pIdxCurrent < pEnd) + { + pDstCurrent[*pIdxCurrent] += *pSrcCurrent; + + pIdxCurrent++; + pSrcCurrent++; + } + } + } + + internal static unsafe void MulElementWiseU(Span src1, Span src2, Span dst) + { + fixed (float* psrc1 = &src1[0]) + fixed (float* psrc2 = &src2[0]) + fixed (float* pdst = dst) + { + float* pSrc1Current = psrc1; + float* pSrc2Current = psrc2; + float* pDstCurrent = pdst; + float* pEnd = pdst + dst.Length; + + while (pDstCurrent + 4 <= pEnd) + { + Vector128 src1Vector = Sse.LoadVector128(pSrc1Current); + Vector128 src2Vector = Sse.LoadVector128(pSrc2Current); + src2Vector = Sse.Multiply(src1Vector, src2Vector); + Sse.Store(pDstCurrent, src2Vector); + + pSrc1Current += 4; + pSrc2Current += 4; + pDstCurrent += 4; + } + + while (pDstCurrent < pEnd) + { + Vector128 src1Vector = Sse.LoadScalarVector128(pSrc1Current); + Vector128 src2Vector = Sse.LoadScalarVector128(pSrc2Current); + src2Vector = Sse.MultiplyScalar(src1Vector, src2Vector); + Sse.StoreScalar(pDstCurrent, src2Vector); + + pSrc1Current++; + pSrc2Current++; + pDstCurrent++; + } + } + } + + internal static unsafe float SumU(Span src) + { + fixed (float* psrc = src) + { + float* pSrcEnd = psrc + src.Length; + float* pSrcCurrent = psrc; + + Vector128 result = Sse.SetZeroVector128(); + + while (pSrcCurrent + 4 < pSrcEnd) + { + result = Sse.Add(result, Sse.LoadVector128(pSrcCurrent)); + pSrcCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pSrcEnd) + { + result = Sse.AddScalar(result, Sse.LoadScalarVector128(pSrcCurrent)); + pSrcCurrent++; + } + + return Sse.ConvertToSingle(result); + } + } + + internal static unsafe float SumSqU(Span src) + { + Vector128 result = Sse.SetZeroVector128(); + + fixed (float* psrc = src) + { + float* pSrcCurrent = psrc; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + result = Sse.Add(result, Sse.Multiply(srcVector, srcVector)); + + pSrcCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + result = Sse.AddScalar(result, Sse.MultiplyScalar(srcVector, srcVector)); + + pSrcCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float SumSqDiffU(float mean, Span src) + { + fixed (float* psrc = src) + { + float* pSrcEnd = psrc + src.Length; + float* pSrcCurrent = psrc; + + Vector128 result = Sse.SetZeroVector128(); + Vector128 meanVector = Sse.SetAllVector128(mean); + + while (pSrcCurrent + 4 <= pSrcEnd) + { + Vector128 x = Sse.LoadVector128(pSrcCurrent); + x = Sse.Subtract(x, meanVector); + result = Sse.Add(result, Sse.Multiply(x, x)); + + pSrcCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pSrcEnd) + { + Vector128 x = Sse.LoadScalarVector128(pSrcCurrent); + x = Sse.SubtractScalar(x, meanVector); + result = Sse.AddScalar(result, Sse.MultiplyScalar(x, x)); + + pSrcCurrent++; + } + + return Sse.ConvertToSingle(result); + } + } + + internal static unsafe float SumAbsU(Span src) + { + Vector128 result = Sse.SetZeroVector128(); + Vector128 mask = GetAbsMask(); + + fixed (float* psrc = src) + { + float* pSrcCurrent = psrc; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + result = Sse.Add(result, Sse.And(srcVector, mask)); + + pSrcCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + result = Sse.AddScalar(result, Sse.And(srcVector, mask)); + + pSrcCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float SumAbsDiffU(float mean, Span src) + { + Vector128 result = Sse.SetZeroVector128(); + Vector128 meanVector = Sse.SetAllVector128(mean); + Vector128 mask = GetAbsMask(); + + fixed (float* psrc = src) + { + float* pSrcCurrent = psrc; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + srcVector = Sse.Subtract(srcVector, meanVector); + result = Sse.Add(result, Sse.And(srcVector, mask)); + + pSrcCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + srcVector = Sse.SubtractScalar(srcVector, meanVector); + result = Sse.AddScalar(result, Sse.And(srcVector, mask)); + + pSrcCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float MaxAbsU(Span src) + { + Vector128 result = Sse.SetZeroVector128(); + Vector128 mask = GetAbsMask(); + + fixed (float* psrc = src) + { + float* pSrcCurrent = psrc; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + result = Sse.Max(result, Sse.And(srcVector, mask)); + + pSrcCurrent += 4; + } + + result = VectorMax(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + result = Sse.MaxScalar(result, Sse.And(srcVector, mask)); + + pSrcCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float MaxAbsDiffU(float mean, Span src) + { + Vector128 result = Sse.SetZeroVector128(); + Vector128 meanVector = Sse.SetAllVector128(mean); + Vector128 mask = GetAbsMask(); + + fixed (float* psrc = src) + { + float* pSrcCurrent = psrc; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + srcVector = Sse.Subtract(srcVector, meanVector); + result = Sse.Max(result, Sse.And(srcVector, mask)); + + pSrcCurrent += 4; + } + + result = VectorMax(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + srcVector = Sse.SubtractScalar(srcVector, meanVector); + result = Sse.MaxScalar(result, Sse.And(srcVector, mask)); + + pSrcCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float DotU(Span src, Span dst) + { + Vector128 result = Sse.SetZeroVector128(); + + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 srcVector = Sse.LoadVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + + result = Sse.Add(result, Sse.Multiply(srcVector, dstVector)); + + pSrcCurrent += 4; + pDstCurrent += 4; + } + + result = VectorSum(in result); + + while (pSrcCurrent < pEnd) + { + Vector128 srcVector = Sse.LoadScalarVector128(pSrcCurrent); + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + + result = Sse.AddScalar(result, Sse.MultiplyScalar(srcVector, dstVector)); + + pSrcCurrent++; + pDstCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float DotSU(Span src, Span dst, Span idx) + { + Vector128 result = Sse.SetZeroVector128(); + + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (int* pidx = idx) + { + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + int* pIdxCurrent = pidx; + int* pEnd = pidx + idx.Length; + + while (pIdxCurrent + 4 <= pEnd) + { + Vector128 srcVector = Load4(pSrcCurrent, pIdxCurrent); + Vector128 dstVector = Sse.LoadVector128(pDstCurrent); + + result = Sse.Add(result, Sse.Multiply(srcVector, dstVector)); + + pIdxCurrent += 4; + pDstCurrent += 4; + } + + result = VectorSum(in result); + + while (pIdxCurrent < pEnd) + { + Vector128 srcVector = Load1(pSrcCurrent, pIdxCurrent); + Vector128 dstVector = Sse.LoadScalarVector128(pDstCurrent); + + result = Sse.AddScalar(result, Sse.MultiplyScalar(srcVector, dstVector)); + + pIdxCurrent++; + pDstCurrent++; + } + } + + return Sse.ConvertToSingle(result); + } + + internal static unsafe float Dist2(Span src, Span dst) + { + Vector128 sqDistanceVector = Sse.SetZeroVector128(); + + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + float* pSrcCurrent = psrc; + float* pDstCurrent = pdst; + float* pEnd = psrc + src.Length; + + while (pSrcCurrent + 4 <= pEnd) + { + Vector128 distanceVector = Sse.Subtract(Sse.LoadVector128(pSrcCurrent), + Sse.LoadVector128(pDstCurrent)); + sqDistanceVector = Sse.Add(sqDistanceVector, + Sse.Multiply(distanceVector, distanceVector)); + + pSrcCurrent += 4; + pDstCurrent += 4; + } + + sqDistanceVector = VectorSum(in sqDistanceVector); + + float norm = Sse.ConvertToSingle(sqDistanceVector); + while (pSrcCurrent < pEnd) + { + float distance = (*pSrcCurrent) - (*pDstCurrent); + norm += distance * distance; + + pSrcCurrent++; + pDstCurrent++; + } + + return norm; + } + } + + internal static unsafe void SdcaL1UpdateU(float primalUpdate, Span src, float threshold, Span v, Span w) + { + fixed (float* psrc = src) + fixed (float* pdst1 = v) + fixed (float* pdst2 = w) + { + float* pSrcEnd = psrc + src.Length; + float* pSrcCurrent = psrc; + float* pDst1Current = pdst1; + float* pDst2Current = pdst2; + + Vector128 xPrimal = Sse.SetAllVector128(primalUpdate); + + Vector128 signMask = Sse.SetAllVector128(-0.0f); // 0x8000 0000 + Vector128 xThreshold = Sse.SetAllVector128(threshold); + + while (pSrcCurrent + 4 <= pSrcEnd) + { + Vector128 xSrc = Sse.LoadVector128(pSrcCurrent); + + Vector128 xDst1 = Sse.LoadVector128(pDst1Current); + xDst1 = Sse.Add(xDst1, Sse.Multiply(xSrc, xPrimal)); + Vector128 xDst2 = GetNewDst(xDst1, signMask, xThreshold); + + Sse.Store(pDst1Current, xDst1); + Sse.Store(pDst2Current, xDst2); + + pSrcCurrent += 4; + pDst1Current += 4; + pDst2Current += 4; + } + + while (pSrcCurrent < pSrcEnd) + { + *pDst1Current += (*pSrcCurrent) * primalUpdate; + float dst1 = *pDst1Current; + *pDst2Current = Math.Abs(dst1) > threshold ? (dst1 > 0 ? dst1 - threshold : dst1 + threshold) : 0; + + pSrcCurrent++; + pDst1Current++; + pDst2Current++; + } + } + } + + internal static unsafe void SdcaL1UpdateSU(float primalUpdate, Span src, Span indices, float threshold, Span v, Span w) + { + fixed (float* psrc = src) + fixed (int* pidx = indices) + fixed (float* pdst1 = v) + fixed (float* pdst2 = w) + { + int* pIdxEnd = pidx + indices.Length; + float* pSrcCurrent = psrc; + int* pIdxCurrent = pidx; + + Vector128 xPrimal = Sse.SetAllVector128(primalUpdate); + + Vector128 signMask = Sse.SetAllVector128(-0.0f); // 0x8000 0000 + Vector128 xThreshold = Sse.SetAllVector128(threshold); + + while (pIdxCurrent + 4 <= pIdxEnd) + { + Vector128 xSrc = Sse.LoadVector128(pSrcCurrent); + + Vector128 xDst1 = Load4(pdst1, pIdxCurrent); + xDst1 = Sse.Add(xDst1, Sse.Multiply(xSrc, xPrimal)); + Vector128 xDst2 = GetNewDst(xDst1, signMask, xThreshold); + + Store4(in xDst1, pdst1, pIdxCurrent); + Store4(in xDst2, pdst2, pIdxCurrent); + + pIdxCurrent += 4; + pSrcCurrent += 4; + } + + while (pIdxCurrent < pIdxEnd) + { + int index = *pIdxCurrent; + pdst1[index] += (*pSrcCurrent) * primalUpdate; + float dst1 = pdst1[index]; + pdst2[index] = Math.Abs(dst1) > threshold ? (dst1 > 0 ? dst1 - threshold : dst1 + threshold) : 0; + + pIdxCurrent++; + pSrcCurrent++; + } + } + } + } +} diff --git a/src/Microsoft.ML.CpuMath/Thunk.cs b/src/Microsoft.ML.CpuMath/Thunk.cs index d7082c8313..1053f75b75 100644 --- a/src/Microsoft.ML.CpuMath/Thunk.cs +++ b/src/Microsoft.ML.CpuMath/Thunk.cs @@ -2,7 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System; using System.Runtime.InteropServices; using System.Runtime.CompilerServices; using System.Security; diff --git a/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs b/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs index 26ec32d3fe..971512cc97 100644 --- a/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs +++ b/src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs @@ -11,6 +11,7 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Utilities; @@ -27,8 +28,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr")] public SubComponent Trainer = new SubComponent("AveragedPerceptron"); - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101)] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] + public IComponentFactory Scorer; [Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "", SortOrder = 102)] public SubComponent Evaluator; @@ -69,14 +70,14 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.LastOccurenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] public bool? CacheData; - [Argument(ArgumentType.Multiple, HelpText = "Transforms to apply prior to splitting the data into folds", ShortName = "prexf")] - public KeyValuePair>[] PreTransform; + [Argument(ArgumentType.Multiple, HelpText = "Transforms to apply prior to splitting the data into folds", ShortName = "prexf", SignatureType = typeof(SignatureDataTransform))] + public KeyValuePair>[] PreTransform; [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The validation data file", ShortName = "valid")] public string ValidationFile; - [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "")] - public SubComponent Calibrator = new SubComponent("PlattCalibration"); + [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "", SignatureType = typeof(SignatureCalibrator))] + public IComponentFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] public int MaxCalibrationExamples = 1000000000; @@ -159,16 +160,18 @@ private void RunCore(IChannel ch, string cmd) string name = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name); if (name == null) { - var args = new GenerateNumberTransform.Arguments(); - args.Column = new[] { new GenerateNumberTransform.Column() { Name = DefaultColumnNames.Name }, }; - args.UseCounter = true; - var options = CmdParser.GetSettings(ch, args, new GenerateNumberTransform.Arguments()); preXf = preXf.Concat( new[] { - new KeyValuePair>( - "", new SubComponent( - GenerateNumberTransform.LoadName, options)) + new KeyValuePair>( + "", ComponentFactoryUtils.CreateFromFunction( + (env, input) => + { + var args = new GenerateNumberTransform.Arguments(); + args.Column = new[] { new GenerateNumberTransform.Column() { Name = DefaultColumnNames.Name }, }; + args.UseCounter = true; + return new GenerateNumberTransform(env, args, input); + })) }).ToArray(); } } @@ -263,7 +266,7 @@ private RoleMappedData ApplyAllTransformsToData(IHostEnvironment env, IChannel c private RoleMappedData CreateRoleMappedData(IHostEnvironment env, IChannel ch, IDataView data, ITrainer trainer) { foreach (var kvp in Args.Transform) - data = kvp.Value.CreateInstance(env, data); + data = kvp.Value.CreateComponent(env, data); var schema = data.Schema; string label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.LabelColumn), Args.LabelColumn, DefaultColumnNames.Label); @@ -380,9 +383,9 @@ public FoldResult(Dictionary metrics, ISchema scoreSchema, Ro private readonly string _splitColumn; private readonly int _numFolds; private readonly SubComponent _trainer; - private readonly SubComponent _scorer; + private readonly IComponentFactory _scorer; private readonly SubComponent _evaluator; - private readonly SubComponent _calibrator; + private readonly IComponentFactory _calibrator; private readonly int _maxCalibrationExamples; private readonly bool _useThreads; private readonly bool? _cacheData; @@ -420,7 +423,7 @@ public FoldHelper( Arguments args, Func createExamples, Func applyTransformsToTestData, - SubComponent scorer, + IComponentFactory scorer, SubComponent evaluator, Func getValidationDataView = null, Func applyTransformsToValidationData = null, @@ -556,11 +559,12 @@ private FoldResult RunFold(int fold) // Score. ch.Trace("Scoring and evaluating"); - var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, _scorer); + ch.Assert(_scorer == null || _scorer is ICommandLineComponentFactory, "CrossValidationCommand should only be used from the command line."); + var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, scorerFactorySettings: _scorer as ICommandLineComponentFactory); ch.AssertValue(bindable); var mapper = bindable.Bind(host, testData.Schema); - var scorerComp = _scorer.IsGood() ? _scorer : ScoreUtils.GetScorerComponent(mapper); - IDataScorerTransform scorePipe = scorerComp.CreateInstance(host, testData.Data, mapper, trainData.Schema); + var scorerComp = _scorer ?? ScoreUtils.GetScorerComponent(mapper); + IDataScorerTransform scorePipe = scorerComp.CreateComponent(host, testData.Data, mapper, trainData.Schema); // Save per-fold model. string modelFileName = ConstructPerFoldName(_outputModelFile, fold); diff --git a/src/Microsoft.ML.Data/Commands/DataCommand.cs b/src/Microsoft.ML.Data/Commands/DataCommand.cs index 2a62d78901..8f489e270f 100644 --- a/src/Microsoft.ML.Data/Commands/DataCommand.cs +++ b/src/Microsoft.ML.Data/Commands/DataCommand.cs @@ -8,6 +8,8 @@ using System.Linq; using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -20,8 +22,8 @@ public static class DataCommand { public abstract class ArgumentsBase { - [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The data loader", ShortName = "loader", SortOrder = 1, NullName = "")] - public SubComponent Loader; + [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The data loader", ShortName = "loader", SortOrder = 1, NullName = "", SignatureType = typeof(SignatureDataLoader))] + public IComponentFactory Loader; [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file", ShortName = "data", SortOrder = 0)] public string DataFile; @@ -51,8 +53,8 @@ public abstract class ArgumentsBase HelpText = "Desired degree of parallelism in the data pipeline", ShortName = "n")] public int? Parallel; - [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Transform", ShortName = "xf")] - public KeyValuePair>[] Transform; + [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Transform", ShortName = "xf", SignatureType = typeof(SignatureDataTransform))] + public KeyValuePair>[] Transform; } public abstract class ImplBase : ICommand @@ -125,6 +127,17 @@ protected void SendTelemetryComponent(IPipe pipe, SubComponent pipe.Send(TelemetryMessage.CreateTrainer(sub.Kind, sub.SubComponentSettings)); } + protected void SendTelemetryComponent(IPipe pipe, IComponentFactory factory) + { + Host.AssertValue(pipe); + Host.AssertValueOrNull(factory); + + if (factory is ICommandLineComponentFactory commandLineFactory) + pipe.Send(TelemetryMessage.CreateTrainer(commandLineFactory.Name, commandLineFactory.GetSettingsString())); + else + pipe.Send(TelemetryMessage.CreateTrainer("Unknown", "Non-ICommandLineComponentFactory object")); + } + protected virtual void SendTelemetryCore(IPipe pipe) { Contracts.AssertValue(pipe); @@ -212,9 +225,9 @@ protected void SaveLoader(IDataLoader loader, string path) LoaderUtils.SaveLoader(loader, file); } - protected IDataLoader CreateAndSaveLoader(string defaultLoader = "TextLoader") + protected IDataLoader CreateAndSaveLoader(Func defaultLoaderFactory = null) { - var loader = CreateLoader(defaultLoader); + var loader = CreateLoader(defaultLoaderFactory); if (!string.IsNullOrWhiteSpace(Args.OutputModelFile)) { using (var file = Host.CreateOutputFile(Args.OutputModelFile)) @@ -268,12 +281,12 @@ protected void LoadModelObjects( } // Next create the loader. - var sub = Args.Loader; + var loaderFactory = Args.Loader; IDataLoader trainPipe = null; - if (sub.IsGood()) + if (loaderFactory != null) { // The loader is overridden from the command line. - pipe = sub.CreateInstance(Host, new MultiFileSource(Args.DataFile)); + pipe = loaderFactory.CreateComponent(Host, new MultiFileSource(Args.DataFile)); if (Args.LoadTransforms == true) { Host.CheckUserArg(!string.IsNullOrWhiteSpace(Args.InputModelFile), nameof(Args.InputModelFile)); @@ -316,9 +329,9 @@ protected void LoadModelObjects( } } - protected IDataLoader CreateLoader(string defaultLoader = "TextLoader") + protected IDataLoader CreateLoader(Func defaultLoaderFactory = null) { - var loader = CreateRawLoader(defaultLoader); + var loader = CreateRawLoader(defaultLoaderFactory); loader = CreateTransformChain(loader); return loader; } @@ -328,13 +341,15 @@ private IDataLoader CreateTransformChain(IDataLoader loader) return CompositeDataLoader.Create(Host, loader, Args.Transform); } - protected IDataLoader CreateRawLoader(string defaultLoader = "TextLoader", string dataFile = null) + protected IDataLoader CreateRawLoader( + Func defaultLoaderFactory = null, + string dataFile = null) { if (string.IsNullOrWhiteSpace(dataFile)) dataFile = Args.DataFile; IDataLoader loader; - if (!string.IsNullOrWhiteSpace(Args.InputModelFile) && !Args.Loader.IsGood()) + if (!string.IsNullOrWhiteSpace(Args.InputModelFile) && Args.Loader == null) { // Load the loader from the data model. using (var file = Host.OpenInputFile(Args.InputModelFile)) @@ -345,8 +360,9 @@ protected IDataLoader CreateRawLoader(string defaultLoader = "TextLoader", strin else { // Either there is no input model file, or there is, but the loader is overridden. - var sub = Args.Loader; - if (!sub.IsGood()) + IMultiStreamSource fileSource = new MultiFileSource(dataFile); + var loaderFactory = Args.Loader; + if (loaderFactory == null) { var ext = Path.GetExtension(dataFile); var isText = @@ -354,12 +370,17 @@ protected IDataLoader CreateRawLoader(string defaultLoader = "TextLoader", strin string.Equals(ext, ".tlc", StringComparison.OrdinalIgnoreCase); var isBinary = string.Equals(ext, ".idv", StringComparison.OrdinalIgnoreCase); var isTranspose = string.Equals(ext, ".tdv", StringComparison.OrdinalIgnoreCase); - sub = - new SubComponent( - isText ? "TextLoader" : isBinary ? "BinaryLoader" : isTranspose ? "TransposeLoader" : defaultLoader); - } - loader = sub.CreateInstance(Host, new MultiFileSource(dataFile)); + return isText ? new TextLoader(Host, new TextLoader.Arguments(), fileSource) : + isBinary ? new BinaryLoader(Host, new BinaryLoader.Arguments(), fileSource) : + isTranspose ? new TransposeLoader(Host, new TransposeLoader.Arguments(), fileSource) : + defaultLoaderFactory != null ? defaultLoaderFactory(Host, fileSource) : + new TextLoader(Host, new TextLoader.Arguments(), fileSource); + } + else + { + loader = loaderFactory.CreateComponent(Host, fileSource); + } if (Args.LoadTransforms == true) { diff --git a/src/Microsoft.ML.Data/Commands/EvaluateCommand.cs b/src/Microsoft.ML.Data/Commands/EvaluateCommand.cs index 77bdf0e32f..c122c65ffa 100644 --- a/src/Microsoft.ML.Data/Commands/EvaluateCommand.cs +++ b/src/Microsoft.ML.Data/Commands/EvaluateCommand.cs @@ -217,7 +217,8 @@ private void RunCore(IChannel ch) Host.AssertValue(ch); ch.Trace("Creating loader"); - IDataView view = CreateAndSaveLoader(IO.BinaryLoader.LoadName); + IDataView view = CreateAndSaveLoader( + (env, source) => new IO.BinaryLoader(env, new IO.BinaryLoader.Arguments(), source)); ch.Trace("Binding columns"); ISchema schema = view.Schema; diff --git a/src/Microsoft.ML.Data/Commands/ScoreCommand.cs b/src/Microsoft.ML.Data/Commands/ScoreCommand.cs index 607bf119d7..879b29d4ce 100644 --- a/src/Microsoft.ML.Data/Commands/ScoreCommand.cs +++ b/src/Microsoft.ML.Data/Commands/ScoreCommand.cs @@ -11,6 +11,7 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -19,6 +20,8 @@ namespace Microsoft.ML.Runtime.Data { + using TScorerFactory = IComponentFactory; + public interface IDataScorerTransform : IDataTransform, ITransformTemplate { } @@ -50,8 +53,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase ShortName = "col", SortOrder = 10)] public KeyValuePair[] CustomColumn; - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use")] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", SignatureType = typeof(SignatureDataScorer))] + public TScorerFactory Scorer; [Argument(ArgumentType.Multiple, HelpText = "The data saver to use")] public SubComponent Saver; @@ -62,8 +65,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to include hidden columns", ShortName = "keep")] public bool KeepHidden; - [Argument(ArgumentType.Multiple, HelpText = "Post processing transform", ShortName = "pxf")] - public KeyValuePair>[] PostTransform; + [Argument(ArgumentType.Multiple, HelpText = "Post processing transform", ShortName = "pxf", SignatureType = typeof(SignatureDataTransform))] + public KeyValuePair>[] PostTransform; [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output all columns or just scores", ShortName = "all")] public bool? OutputAllColumns; @@ -104,7 +107,8 @@ private void RunCore(IChannel ch) ch.Trace("Creating pipeline"); var scorer = Args.Scorer; - var bindable = ScoreUtils.GetSchemaBindableMapper(Host, predictor, scorer); + ch.Assert(scorer == null || scorer is ICommandLineComponentFactory, "ScoreCommand should only be used from the command line."); + var bindable = ScoreUtils.GetSchemaBindableMapper(Host, predictor, scorerFactorySettings: scorer as ICommandLineComponentFactory); ch.AssertValue(bindable); // REVIEW: We probably ought to prefer role mappings from the training schema. @@ -116,11 +120,11 @@ private void RunCore(IChannel ch) var schema = new RoleMappedSchema(loader.Schema, label: null, feature: feat, group: group, custom: customCols, opt: true); var mapper = bindable.Bind(Host, schema); - if (!scorer.IsGood()) + if (scorer == null) scorer = ScoreUtils.GetScorerComponent(mapper); loader = CompositeDataLoader.ApplyTransform(Host, loader, "Scorer", scorer.ToString(), - (env, view) => scorer.CreateInstance(env, view, mapper, trainSchema)); + (env, view) => scorer.CreateComponent(env, view, mapper, trainSchema)); loader = CompositeDataLoader.Create(Host, loader, Args.PostTransform); @@ -224,13 +228,20 @@ public static class ScoreUtils { public static IDataScorerTransform GetScorer(IPredictor predictor, RoleMappedData data, IHostEnvironment env, RoleMappedSchema trainSchema) { - var sc = GetScorerComponentAndMapper(predictor, null, data.Schema, env, out var mapper); - return sc.CreateInstance(env, data.Data, mapper, trainSchema); + var sc = GetScorerComponentAndMapper(predictor, null, data.Schema, env, null, out var mapper); + return sc.CreateComponent(env, data.Data, mapper, trainSchema); } - public static IDataScorerTransform GetScorer(SubComponent scorer, - IPredictor predictor, IDataView input, string featureColName, string groupColName, - IEnumerable> customColumns, IHostEnvironment env, RoleMappedSchema trainSchema) + public static IDataScorerTransform GetScorer( + TScorerFactory scorer, + IPredictor predictor, + IDataView input, + string featureColName, + string groupColName, + IEnumerable> customColumns, + IHostEnvironment env, + RoleMappedSchema trainSchema, + IComponentFactory mapperFactory = null) { Contracts.CheckValue(env, nameof(env)); env.CheckValueOrNull(scorer); @@ -242,24 +253,28 @@ public static IDataScorerTransform GetScorer(SubComponent - /// Determines the scorer subcomponent (if the given one is null or empty), and creates the schema bound mapper. + /// Determines the scorer component factory (if the given one is null or empty), and creates the schema bound mapper. /// - private static SubComponent GetScorerComponentAndMapper( - IPredictor predictor, SubComponent scorer, - RoleMappedSchema schema, IHostEnvironment env, out ISchemaBoundMapper mapper) + private static TScorerFactory GetScorerComponentAndMapper( + IPredictor predictor, + TScorerFactory scorerFactory, + RoleMappedSchema schema, + IHostEnvironment env, + IComponentFactory mapperFactory, + out ISchemaBoundMapper mapper) { Contracts.AssertValue(env); - var bindable = GetSchemaBindableMapper(env, predictor, scorer); + var bindable = GetSchemaBindableMapper(env, predictor, mapperFactory, scorerFactory as ICommandLineComponentFactory); env.AssertValue(bindable); mapper = bindable.Bind(env, schema); - if (scorer.IsGood()) - return scorer; + if (scorerFactory != null) + return scorerFactory; return GetScorerComponent(mapper); } @@ -268,44 +283,84 @@ private static SubComponent GetScorer /// metadata on the first column of the mapper. If that text is found and maps to a scorer loadable class, /// that component is used. Otherwise, the GenericScorer is used. /// - public static SubComponent GetScorerComponent(ISchemaBoundMapper mapper) + /// The schema bound mapper to get the default scorer.. + /// An optional suffix to append to the default column names. + public static TScorerFactory GetScorerComponent( + ISchemaBoundMapper mapper, + string suffix = null) { Contracts.AssertValue(mapper); - string loadName = null; + ComponentCatalog.LoadableClassInfo info = null; DvText scoreKind = default; if (mapper.OutputSchema.ColumnCount > 0 && mapper.OutputSchema.TryGetMetadata(TextType.Instance, MetadataUtils.Kinds.ScoreColumnKind, 0, ref scoreKind) && scoreKind.HasChars) { - loadName = scoreKind.ToString(); - var info = ComponentCatalog.GetLoadableClassInfo(loadName); + var loadName = scoreKind.ToString(); + info = ComponentCatalog.GetLoadableClassInfo(loadName); if (info == null || !typeof(IDataScorerTransform).IsAssignableFrom(info.Type)) - loadName = null; + info = null; } - if (loadName == null) - loadName = GenericScorer.LoadName; - return new SubComponent(loadName); + + Func factoryFunc; + if (info == null) + { + factoryFunc = (env, data, innerMapper, trainSchema) => + new GenericScorer( + env, + new GenericScorer.Arguments() { Suffix = suffix }, + data, + innerMapper, + trainSchema); + } + else + { + factoryFunc = (env, data, innerMapper, trainSchema) => + { + object args = info.CreateArguments(); + if (args is ScorerArgumentsBase scorerArgs) + { + scorerArgs.Suffix = suffix; + } + return (IDataScorerTransform)info.CreateInstance( + env, + args, + new object[] { data, innerMapper, trainSchema }); + }; + } + + return ComponentFactoryUtils.CreateFromFunction(factoryFunc); } /// - /// Given a predictor and an optional scorer SubComponent, produces a compatible ISchemaBindableMapper. - /// First, it tries to instantiate the bindable mapper using the + /// Given a predictor, an optional mapper factory, and an optional scorer factory settings, + /// produces a compatible ISchemaBindableMapper. + /// First, it tries to instantiate the bindable mapper using the mapper factory. + /// Next, it tries to instantiate the bindable mapper using the /// (this will only succeed if there's a registered BindableMapper creation method with load name equal to the one /// of the scorer). /// If the above fails, it checks whether the predictor implements /// directly. /// If this also isn't true, it will create a 'matching' standard mapper. /// - public static ISchemaBindableMapper GetSchemaBindableMapper(IHostEnvironment env, IPredictor predictor, - SubComponent scorerSettings) + public static ISchemaBindableMapper GetSchemaBindableMapper( + IHostEnvironment env, + IPredictor predictor, + IComponentFactory mapperFactory = null, + ICommandLineComponentFactory scorerFactorySettings = null) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(predictor, nameof(predictor)); - env.CheckValueOrNull(scorerSettings); + env.CheckValueOrNull(mapperFactory); + env.CheckValueOrNull(scorerFactorySettings); + + // if the mapperFactory was supplied, use it + if (mapperFactory != null) + return mapperFactory.CreateComponent(env, predictor); // See if we can instantiate a mapper using scorer arguments. - if (scorerSettings.IsGood() && TryCreateBindableFromScorer(env, predictor, scorerSettings, out var bindable)) + if (scorerFactorySettings != null && TryCreateBindableFromScorer(env, predictor, scorerFactorySettings, out var bindable)) return bindable; // The easy case is that the predictor implements the interface. @@ -321,15 +376,15 @@ public static ISchemaBindableMapper GetSchemaBindableMapper(IHostEnvironment env } private static bool TryCreateBindableFromScorer(IHostEnvironment env, IPredictor predictor, - SubComponent scorerSettings, out ISchemaBindableMapper bindable) + ICommandLineComponentFactory scorerSettings, out ISchemaBindableMapper bindable) { Contracts.AssertValue(env); env.AssertValue(predictor); - env.Assert(scorerSettings.IsGood()); + env.AssertValue(scorerSettings); // Try to find a mapper factory method with the same loadname as the scorer settings. - var mapperComponent = new SubComponent(scorerSettings.Kind, scorerSettings.Settings); - return ComponentCatalog.TryCreateInstance(env, out bindable, mapperComponent, predictor); + return ComponentCatalog.TryCreateInstance( + env, out bindable, scorerSettings.Name, scorerSettings.GetSettingsString(), predictor); } } } \ No newline at end of file diff --git a/src/Microsoft.ML.Data/Commands/TestCommand.cs b/src/Microsoft.ML.Data/Commands/TestCommand.cs index d0ebbd5a05..5a4b7671aa 100644 --- a/src/Microsoft.ML.Data/Commands/TestCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TestCommand.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; [assembly: LoadableClass(TestCommand.Summary, typeof(TestCommand), typeof(TestCommand.Arguments), typeof(SignatureCommand), @@ -37,8 +38,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.LastOccurenceWins, HelpText = "Columns with custom kinds declared through key assignments, e.g., col[Kind]=Name to assign column named 'Name' kind 'Kind'", ShortName = "col", SortOrder = 10)] public KeyValuePair[] CustomColumn; - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101)] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] + public IComponentFactory Scorer; [Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "", SortOrder = 102)] public SubComponent Evaluator; @@ -107,6 +108,7 @@ private void RunCore(IChannel ch) // Score. ch.Trace("Scoring and evaluating"); + ch.Assert(Args.Scorer == null || Args.Scorer is ICommandLineComponentFactory, "TestCommand should only be used from the command line."); IDataScorerTransform scorePipe = ScoreUtils.GetScorer(Args.Scorer, predictor, loader, features, group, customCols, Host, trainSchema); // Evaluate. diff --git a/src/Microsoft.ML.Data/Commands/TrainCommand.cs b/src/Microsoft.ML.Data/Commands/TrainCommand.cs index 69370ad3ef..0cb5bfb4e9 100644 --- a/src/Microsoft.ML.Data/Commands/TrainCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TrainCommand.cs @@ -13,6 +13,7 @@ using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -68,8 +69,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.LastOccurenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] public bool? CacheData; - [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "")] - public SubComponent Calibrator = new SubComponent("PlattCalibration"); + [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "", SignatureType = typeof(SignatureCalibrator))] + public IComponentFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] public int MaxCalibrationExamples = 1000000000; @@ -235,9 +236,9 @@ public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData } public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, string name, RoleMappedData validData, - SubComponent calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inputPredictor = null) + IComponentFactory calibrator, int maxCalibrationExamples, bool? cacheData, IPredictor inputPredictor = null) { - ICalibratorTrainer caliTrainer = !calibrator.IsGood() ? null : calibrator.CreateInstance(env); + ICalibratorTrainer caliTrainer = calibrator?.CreateComponent(env); return TrainCore(env, ch, data, trainer, name, validData, caliTrainer, maxCalibrationExamples, cacheData, inputPredictor); } diff --git a/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs b/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs index 03ee7cdf12..bc99d60eb3 100644 --- a/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs +++ b/src/Microsoft.ML.Data/Commands/TrainTestCommand.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -26,8 +27,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.Multiple, HelpText = "Trainer to use", ShortName = "tr")] public SubComponent Trainer = new SubComponent("AveragedPerceptron"); - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101)] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SortOrder = 101, SignatureType = typeof(SignatureDataScorer))] + public IComponentFactory Scorer; [Argument(ArgumentType.Multiple, HelpText = "Evaluator to use", ShortName = "eval", NullName = "", SortOrder = 102)] public SubComponent Evaluator; @@ -62,8 +63,8 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.LastOccurenceWins, HelpText = "Whether we should cache input training data", ShortName = "cache")] public bool? CacheData; - [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "")] - public SubComponent Calibrator = new SubComponent("PlattCalibration"); + [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "", SignatureType = typeof(SignatureCalibrator))] + public IComponentFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] public int MaxCalibrationExamples = 1000000000; @@ -182,6 +183,7 @@ private void RunCore(IChannel ch, string cmd) // Score. ch.Trace("Scoring and evaluating"); + ch.Assert(Args.Scorer == null || Args.Scorer is ICommandLineComponentFactory, "TrainTestCommand should only be used from the command line."); IDataScorerTransform scorePipe = ScoreUtils.GetScorer(Args.Scorer, predictor, testPipe, features, group, customCols, Host, data.Schema); // Evaluate. diff --git a/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs index a2ab3a7b16..d83af5d824 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/CompositeDataLoader.cs @@ -14,6 +14,7 @@ using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.EntryPoints; [assembly: LoadableClass(typeof(IDataLoader), typeof(CompositeDataLoader), typeof(CompositeDataLoader.Arguments), typeof(SignatureDataLoader), "Composite Data Loader", "CompositeDataLoader", "Composite", "PipeData", "Pipe", "PipeDataLoader")] @@ -34,11 +35,11 @@ public sealed class CompositeDataLoader : IDataLoader, ITransposeDataView { public sealed class Arguments { - [Argument(ArgumentType.Multiple, HelpText = "The data loader", ShortName = "loader")] - public SubComponent Loader; + [Argument(ArgumentType.Multiple, HelpText = "The data loader", ShortName = "loader", SignatureType = typeof(SignatureDataLoader))] + public IComponentFactory Loader; - [Argument(ArgumentType.Multiple, HelpText = "Transform", ShortName = "xf")] - public KeyValuePair>[] Transform; + [Argument(ArgumentType.Multiple, HelpText = "Transform", ShortName = "xf", SignatureType = typeof(SignatureDataTransform))] + public KeyValuePair>[] Transform; } private struct TransformEx @@ -98,10 +99,10 @@ public static IDataLoader Create(IHostEnvironment env, Arguments args, IMultiStr var h = env.Register(RegistrationName); h.CheckValue(args, nameof(args)); - h.CheckUserArg(args.Loader.IsGood(), nameof(args.Loader)); + h.CheckValue(args.Loader, nameof(args.Loader)); h.CheckValue(files, nameof(files)); - var loader = args.Loader.CreateInstance(h, files); + var loader = args.Loader.CreateComponent(h, files); return CreateCore(h, loader, args.Transform); } @@ -111,7 +112,7 @@ public static IDataLoader Create(IHostEnvironment env, Arguments args, IMultiStr /// If there are no transforms, the is returned. /// public static IDataLoader Create(IHostEnvironment env, IDataLoader srcLoader, - params KeyValuePair>[] transformArgs) + params KeyValuePair>[] transformArgs) { Contracts.CheckValue(env, nameof(env)); var h = env.Register(RegistrationName); @@ -122,7 +123,7 @@ public static IDataLoader Create(IHostEnvironment env, IDataLoader srcLoader, } private static IDataLoader CreateCore(IHost host, IDataLoader srcLoader, - KeyValuePair>[] transformArgs) + KeyValuePair>[] transformArgs) { Contracts.AssertValue(host, "host"); host.AssertValue(srcLoader, "srcLoader"); @@ -131,8 +132,15 @@ private static IDataLoader CreateCore(IHost host, IDataLoader srcLoader, if (Utils.Size(transformArgs) == 0) return srcLoader; + string GetTagData(IComponentFactory factory) + { + // When coming from the command line, preserve the string arguments. + // For other factories, we aren't able to get the string. + return (factory as ICommandLineComponentFactory)?.ToString(); + } + var tagData = transformArgs - .Select(x => new KeyValuePair(x.Key, x.Value.ToString())) + .Select(x => new KeyValuePair(x.Key, GetTagData(x.Value))) .ToArray(); // Warn if tags coincide with ones already present in the loader. @@ -152,7 +160,7 @@ private static IDataLoader CreateCore(IHost host, IDataLoader srcLoader, } return ApplyTransformsCore(host, srcLoader, tagData, - (prov, index, data) => transformArgs[index].Value.CreateInstance(prov, data)); + (env, index, data) => transformArgs[index].Value.CreateComponent(env, data)); } /// diff --git a/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs index 10bf816dc1..70be2bcb92 100644 --- a/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs +++ b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs @@ -13,6 +13,7 @@ using Microsoft.ML.Runtime.Data.Conversion; using Microsoft.ML.Runtime.Data.IO; using Microsoft.ML.Runtime.Data.Utilities; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -74,8 +75,8 @@ public class Arguments [Argument(ArgumentType.AtMostOnce, HelpText = "Path parser to extract column name/value pairs from the file path.", ShortName = "parser")] public IPartitionedPathParserFactory PathParserFactory = new ParquetPartitionedPathParserFactory(); - [Argument(ArgumentType.Multiple, HelpText = "The data loader.")] - public SubComponent Loader; + [Argument(ArgumentType.Multiple, HelpText = "The data loader.", SignatureType = typeof(SignatureDataLoader))] + public IComponentFactory Loader; } public sealed class Column @@ -173,6 +174,7 @@ public PartitionedFileLoader(IHostEnvironment env, Arguments args, IMultiStreamS Contracts.CheckValue(env, nameof(env)); _host = env.Register(RegistrationName); _host.CheckValue(args, nameof(args)); + _host.CheckValue(args.Loader, nameof(args.Loader)); _host.CheckValue(files, nameof(files)); _pathParser = args.PathParserFactory.CreateComponent(_host); @@ -180,7 +182,7 @@ public PartitionedFileLoader(IHostEnvironment env, Arguments args, IMultiStreamS _files = files; - var subLoader = args.Loader.CreateInstance(_host, _files); + var subLoader = args.Loader.CreateComponent(_host, _files); _subLoaderBytes = SaveLoaderToBytes(subLoader); string relativePath = GetRelativePath(args.BasePath, files); diff --git a/src/Microsoft.ML.Data/Depricated/Vector/VBufferMathUtils.cs b/src/Microsoft.ML.Data/Depricated/Vector/VBufferMathUtils.cs index 045c5d30a1..c8a7d3d241 100644 --- a/src/Microsoft.ML.Data/Depricated/Vector/VBufferMathUtils.cs +++ b/src/Microsoft.ML.Data/Depricated/Vector/VBufferMathUtils.cs @@ -22,7 +22,7 @@ public static Float NormSquared(in VBuffer a) { if (a.Count == 0) return 0; - return SseUtils.SumSq(a.Values, 0, a.Count); + return CpuMathUtils.SumSq(a.Values, 0, a.Count); } /// @@ -30,7 +30,7 @@ public static Float NormSquared(in VBuffer a) /// public static Float NormSquared(Float[] a, int offset, int count) { - return SseUtils.SumSq(a, offset, count); + return CpuMathUtils.SumSq(a, offset, count); } /// @@ -50,7 +50,7 @@ public static Float L1Norm(ref VBuffer a) { if (a.Count == 0) return 0; - return SseUtils.SumAbs(a.Values, 0, a.Count); + return CpuMathUtils.SumAbs(a.Values, 0, a.Count); } /// @@ -61,7 +61,7 @@ public static Float MaxNorm(ref VBuffer a) { if (a.Count == 0) return 0; - return SseUtils.MaxAbs(a.Values, 0, a.Count); + return CpuMathUtils.MaxAbs(a.Values, 0, a.Count); } /// @@ -71,7 +71,7 @@ public static Float Sum(ref VBuffer a) { if (a.Count == 0) return 0; - return SseUtils.Sum(a.Values, 0, a.Count); + return CpuMathUtils.Sum(a.Values, 0, a.Count); } /// @@ -84,7 +84,7 @@ public static void ScaleBy(ref VBuffer dst, Float c) if (c == 1 || dst.Count == 0) return; if (c != 0) - SseUtils.Scale(c, dst.Values, dst.Count); + CpuMathUtils.Scale(c, dst.Values, dst.Count); else // Maintain density of dst. Array.Clear(dst.Values, 0, dst.Count); // REVIEW: Any benefit in sparsifying? @@ -114,7 +114,7 @@ public static void ScaleBy(in VBuffer src, ref VBuffer dst, Float if (c == 0) Array.Clear(dstValues, 0, length); else - SseUtils.Scale(c, src.Values, dstValues, length); + CpuMathUtils.Scale(c, src.Values, dstValues, length); dst = new VBuffer(length, dstValues, dst.Indices); } else @@ -124,7 +124,7 @@ public static void ScaleBy(in VBuffer src, ref VBuffer dst, Float if (c == 0) Array.Clear(dstValues, 0, count); else - SseUtils.Scale(c, src.Values, dstValues, count); + CpuMathUtils.Scale(c, src.Values, dstValues, count); dst = new VBuffer(length, count, dstValues, dstIndices); } } @@ -142,9 +142,9 @@ public static void Add(ref VBuffer src, ref VBuffer dst) if (dst.IsDense) { if (src.IsDense) - SseUtils.Add(src.Values, dst.Values, src.Length); + CpuMathUtils.Add(src.Values, dst.Values, src.Length); else - SseUtils.Add(src.Values, src.Indices, dst.Values, src.Count); + CpuMathUtils.Add(src.Values, src.Indices, dst.Values, src.Count); return; } // REVIEW: Should we use SSE for any of these possibilities? @@ -168,9 +168,9 @@ public static void AddMult(ref VBuffer src, Float c, ref VBuffer d if (dst.IsDense) { if (src.IsDense) - SseUtils.AddScale(c, src.Values, dst.Values, src.Length); + CpuMathUtils.AddScale(c, src.Values, dst.Values, src.Length); else - SseUtils.AddScale(c, src.Values, src.Indices, dst.Values, src.Count); + CpuMathUtils.AddScale(c, src.Values, src.Indices, dst.Values, src.Count); return; } // REVIEW: Should we use SSE for any of these possibilities? @@ -197,7 +197,7 @@ public static void AddMult(ref VBuffer src, Float c, ref VBuffer d if (dst.IsDense && src.IsDense) { Float[] resValues = Utils.Size(res.Values) >= length ? res.Values : new Float[length]; - SseUtils.AddScaleCopy(c, src.Values, dst.Values, resValues, length); + CpuMathUtils.AddScaleCopy(c, src.Values, dst.Values, resValues, length); res = new VBuffer(length, resValues, res.Indices); return; } @@ -239,9 +239,9 @@ public static void AddMultWithOffset(ref VBuffer src, Float c, ref VBuffe { // This is by far the most common case. if (src.IsDense) - SseUtils.AddScale(c, src.Values, dst.Values, offset, src.Count); + CpuMathUtils.AddScale(c, src.Values, dst.Values, offset, src.Count); else - SseUtils.AddScale(c, src.Values, src.Indices, dst.Values, offset, src.Count); + CpuMathUtils.AddScale(c, src.Values, src.Indices, dst.Values, offset, src.Count); return; } // REVIEW: Perhaps implementing an ApplyInto with an offset would be more diff --git a/src/Microsoft.ML.Data/Depricated/Vector/VectorUtils.cs b/src/Microsoft.ML.Data/Depricated/Vector/VectorUtils.cs index 5b46aad0b8..5b2df5b486 100644 --- a/src/Microsoft.ML.Data/Depricated/Vector/VectorUtils.cs +++ b/src/Microsoft.ML.Data/Depricated/Vector/VectorUtils.cs @@ -24,7 +24,7 @@ public static Float DotProduct(Float[] a, Float[] b) { Contracts.Check(Utils.Size(a) == Utils.Size(b), "Arrays must have the same length"); Contracts.Check(Utils.Size(a) > 0); - return SseUtils.DotProductDense(a, b, a.Length); + return CpuMathUtils.DotProductDense(a, b, a.Length); } public static Float DotProduct(Float[] a, ref VBuffer b) @@ -33,8 +33,8 @@ public static Float DotProduct(Float[] a, ref VBuffer b) if (b.Count == 0) return 0; if (b.IsDense) - return SseUtils.DotProductDense(a, b.Values, b.Length); - return SseUtils.DotProductSparse(a, b.Values, b.Indices, b.Count); + return CpuMathUtils.DotProductDense(a, b.Values, b.Length); + return CpuMathUtils.DotProductSparse(a, b.Values, b.Indices, b.Count); } public static Float DotProduct(ref VBuffer a, ref VBuffer b) @@ -47,12 +47,12 @@ public static Float DotProduct(ref VBuffer a, ref VBuffer b) if (a.IsDense) { if (b.IsDense) - return SseUtils.DotProductDense(a.Values, b.Values, a.Length); - return SseUtils.DotProductSparse(a.Values, b.Values, b.Indices, b.Count); + return CpuMathUtils.DotProductDense(a.Values, b.Values, a.Length); + return CpuMathUtils.DotProductSparse(a.Values, b.Values, b.Indices, b.Count); } if (b.IsDense) - return SseUtils.DotProductSparse(b.Values, a.Values, a.Indices, a.Count); + return CpuMathUtils.DotProductSparse(b.Values, a.Values, a.Indices, a.Count); return DotProductSparse(a.Values, a.Indices, 0, a.Count, b.Values, b.Indices, 0, b.Count, 0); } @@ -159,7 +159,7 @@ public static void MulElementWise(ref VBuffer a, ref VBuffer dst) Contracts.Check(a.Length == dst.Length, "Vectors must have the same dimensionality."); if (a.IsDense && dst.IsDense) - SseUtils.MulElementWise(a.Values, dst.Values, dst.Values, a.Length); + CpuMathUtils.MulElementWise(a.Values, dst.Values, dst.Values, a.Length); else VBufferUtils.ApplyWithEitherDefined(ref a, ref dst, (int ind, Float v1, ref Float v2) => { v2 *= v1; }); } @@ -228,11 +228,11 @@ private static Float L2DistSquaredHalfSparse(Float[] valuesA, int lengthA, Float Contracts.Assert(0 <= countB && countB <= Utils.Size(indicesB)); Contracts.Assert(countB <= Utils.Size(valuesB)); - var normA = SseUtils.SumSq(valuesA, 0, lengthA); + var normA = CpuMathUtils.SumSq(valuesA, 0, lengthA); if (countB == 0) return normA; - var normB = SseUtils.SumSq(valuesB, 0, countB); - var dotP = SseUtils.DotProductSparse(valuesA, valuesB, indicesB, countB); + var normB = CpuMathUtils.SumSq(valuesB, 0, countB); + var dotP = CpuMathUtils.DotProductSparse(valuesA, valuesB, indicesB, countB); var res = normA + normB - 2 * dotP; return res < 0 ? 0 : res; } @@ -246,7 +246,7 @@ private static Float L2DiffSquaredDense(Float[] valuesA, Float[] valuesB, int le if (length == 0) return 0; - return SseUtils.L2DistSquared(valuesA, valuesB, length); + return CpuMathUtils.L2DistSquared(valuesA, valuesB, length); } /// @@ -267,8 +267,8 @@ public static Float DotProductWithOffset(ref VBuffer a, int offset, ref V if (a.IsDense) { if (b.IsDense) - return SseUtils.DotProductDense(a.Values, offset, b.Values, b.Length); - return SseUtils.DotProductSparse(a.Values, offset, b.Values, b.Indices, b.Count); + return CpuMathUtils.DotProductDense(a.Values, offset, b.Values, b.Length); + return CpuMathUtils.DotProductSparse(a.Values, offset, b.Values, b.Indices, b.Count); } else { @@ -314,8 +314,8 @@ public static Float DotProductWithOffset(Float[] a, int offset, ref VBuffer @@ -452,7 +452,7 @@ public static void AddMult(ref VBuffer src, Float[] dst, Float c) return; if (src.IsDense) - SseUtils.AddScale(c, src.Values, dst, src.Count); + CpuMathUtils.AddScale(c, src.Values, dst, src.Count); else { for (int i = 0; i < src.Count; i++) @@ -502,7 +502,7 @@ public static void AddMult(Float[] src, Float[] dst, Float c) if (c == 0) return; - SseUtils.AddScale(c, src, dst, src.Length); + CpuMathUtils.AddScale(c, src, dst, src.Length); } /// @@ -510,7 +510,7 @@ public static void AddMult(Float[] src, Float[] dst, Float c) /// public static Float Norm(Float[] a) { - return MathUtils.Sqrt(SseUtils.SumSq(a, a.Length)); + return MathUtils.Sqrt(CpuMathUtils.SumSq(a, a.Length)); } /// @@ -520,7 +520,7 @@ public static Float Sum(Float[] a) { if (a == null || a.Length == 0) return 0; - return SseUtils.Sum(a, a.Length); + return CpuMathUtils.Sum(a, a.Length); } /// @@ -534,7 +534,7 @@ public static void ScaleBy(Float[] dst, Float c) return; if (c != 0) - SseUtils.Scale(c, dst, dst.Length); + CpuMathUtils.Scale(c, dst, dst.Length); else Array.Clear(dst, 0, dst.Length); } diff --git a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs index 312a92bccc..260ff37f26 100644 --- a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs +++ b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs @@ -79,14 +79,12 @@ public static Output Score(IHostEnvironment env, Input input) using (var ch = host.Start("Creating scoring pipeline")) { ch.Trace("Creating pipeline"); - var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, scorerSettings: null); + var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor); ch.AssertValue(bindable); var mapper = bindable.Bind(host, data.Schema); - var scorer = ScoreUtils.GetScorerComponent(mapper); - Contracts.Assert(string.IsNullOrEmpty(scorer.SubComponentSettings)); - scorer.SubComponentSettings = string.Format("suffix={{{0}}}", input.Suffix); - scoredPipe = scorer.CreateInstance(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); + var scorer = ScoreUtils.GetScorerComponent(mapper, input.Suffix); + scoredPipe = scorer.CreateComponent(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); ch.Done(); } @@ -132,12 +130,12 @@ public static Output MakeScoringTransform(IHostEnvironment env, ModelInput input using (var ch = host.Start("Creating scoring pipeline")) { ch.Trace("Creating pipeline"); - var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, scorerSettings: null); + var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor); ch.AssertValue(bindable); var mapper = bindable.Bind(host, data.Schema); var scorer = ScoreUtils.GetScorerComponent(mapper); - scoredPipe = scorer.CreateInstance(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); + scoredPipe = scorer.CreateComponent(host, data.Data, mapper, input.PredictorModel.GetTrainingSchema(host)); ch.Done(); } diff --git a/src/Microsoft.ML.Data/Prediction/Calibrator.cs b/src/Microsoft.ML.Data/Prediction/Calibrator.cs index 237afb400e..0e4b9f07e9 100644 --- a/src/Microsoft.ML.Data/Prediction/Calibrator.cs +++ b/src/Microsoft.ML.Data/Prediction/Calibrator.cs @@ -622,14 +622,14 @@ private static VersionInfo GetVersionInfo() public SchemaBindableCalibratedPredictor(IHostEnvironment env, IPredictorProducing predictor, ICalibrator calibrator) : base(env, LoaderSignature, predictor, calibrator) { - _bindable = ScoreUtils.GetSchemaBindableMapper(Host, SubPredictor, null); + _bindable = ScoreUtils.GetSchemaBindableMapper(Host, SubPredictor); _whatTheFeature = SubPredictor as IWhatTheFeatureValueMapper; } private SchemaBindableCalibratedPredictor(IHostEnvironment env, ModelLoadContext ctx) : base(env, LoaderSignature, GetPredictor(env, ctx), GetCalibrator(env, ctx)) { - _bindable = ScoreUtils.GetSchemaBindableMapper(Host, SubPredictor, null); + _bindable = ScoreUtils.GetSchemaBindableMapper(Host, SubPredictor); _whatTheFeature = SubPredictor as IWhatTheFeatureValueMapper; } @@ -717,7 +717,7 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat return false; } - var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor, null); + var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor); var bound = bindable.Bind(env, schema); var outputSchema = bound.OutputSchema; int scoreCol; diff --git a/src/Microsoft.ML.Data/Transforms/TermTransform.cs b/src/Microsoft.ML.Data/Transforms/TermTransform.cs index 6eaf48e995..6365e27a54 100644 --- a/src/Microsoft.ML.Data/Transforms/TermTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/TermTransform.cs @@ -117,8 +117,8 @@ public abstract class ArgumentsBase : TransformInputBase [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Data file containing the terms", ShortName = "data", SortOrder = 110, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] public string DataFile; - [Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "", SortOrder = 111, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] - public SubComponent Loader; + [Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "", SortOrder = 111, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureDataLoader))] + public IComponentFactory Loader; [Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the terms", ShortName = "termCol", SortOrder = 112, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] public string TermsColumn; @@ -309,12 +309,19 @@ private static TermMap CreateFileTermMap(IHostEnvironment env, IChannel ch, Argu string file = args.DataFile; // First column using the file. string src = args.TermsColumn; - var sub = args.Loader; + IMultiStreamSource fileSource = new MultiFileSource(file); + + var loaderFactory = args.Loader; // If the user manually specifies a loader, or this is already a pre-processed binary // file, then we assume the user knows what they're doing and do not attempt to convert // to the desired type ourselves. bool autoConvert = false; - if (!sub.IsGood()) + IDataLoader loader; + if (loaderFactory != null) + { + loader = loaderFactory.CreateComponent(env, fileSource); + } + else { // Determine the default loader from the extension. var ext = Path.GetExtension(file); @@ -326,11 +333,11 @@ private static TermMap CreateFileTermMap(IHostEnvironment env, IChannel ch, Argu ch.CheckUserArg(!string.IsNullOrWhiteSpace(src), nameof(args.TermsColumn), "Must be specified"); if (isBinary) - sub = new SubComponent("BinaryLoader"); + loader = new BinaryLoader(env, new BinaryLoader.Arguments(), fileSource); else { ch.Assert(isTranspose); - sub = new SubComponent("TransposeLoader"); + loader = new TransposeLoader(env, new TransposeLoader.Arguments(), fileSource); } } else @@ -341,7 +348,21 @@ private static TermMap CreateFileTermMap(IHostEnvironment env, IChannel ch, Argu "{0} should not be specified when default loader is TextLoader. Ignoring {0}={1}", nameof(Arguments.TermsColumn), src); } - sub = new SubComponent("TextLoader", "sep=tab col=Term:TX:0"); + loader = new TextLoader(env, + new TextLoader.Arguments() + { + Separator = "tab", + Column = new[] + { + new TextLoader.Column() + { + Name ="Term", + Type = DataKind.TX, + Source = new[] { new TextLoader.Range() { Min = 0 } } + } + } + }, + fileSource); src = "Term"; autoConvert = true; } @@ -349,8 +370,6 @@ private static TermMap CreateFileTermMap(IHostEnvironment env, IChannel ch, Argu ch.AssertNonEmpty(src); int colSrc; - var loader = sub.CreateInstance(env, new MultiFileSource(file)); - if (!loader.Schema.TryGetColumnIndex(src, out colSrc)) throw ch.ExceptUserArg(nameof(args.TermsColumn), "Unknown column '{0}'", src); var typeSrc = loader.Schema.GetColumnType(colSrc); @@ -395,7 +414,7 @@ private static TermMap[] Train(IHostEnvironment env, IChannel ch, ColInfo[] info ch.AssertValue(trainingData); if ((args.Term != null || !string.IsNullOrEmpty(args.Terms)) && - (!string.IsNullOrWhiteSpace(args.DataFile) || args.Loader.IsGood() || + (!string.IsNullOrWhiteSpace(args.DataFile) || args.Loader != null || !string.IsNullOrWhiteSpace(args.TermsColumn))) { ch.Warning("Explicit term list specified. Data file arguments will be ignored"); diff --git a/src/Microsoft.ML.Data/Transforms/TrainAndScoreTransform.cs b/src/Microsoft.ML.Data/Transforms/TrainAndScoreTransform.cs index bba2f58256..f4a8a5722f 100644 --- a/src/Microsoft.ML.Data/Transforms/TrainAndScoreTransform.cs +++ b/src/Microsoft.ML.Data/Transforms/TrainAndScoreTransform.cs @@ -36,8 +36,8 @@ public sealed class Arguments : TransformInputBase ShortName = "col", SortOrder = 101, Purpose = SpecialPurpose.ColumnSelector)] public KeyValuePair[] CustomColumn; - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "")] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SignatureType = typeof(SignatureDataScorer))] + public IComponentFactory Scorer; [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Predictor model file used in scoring", ShortName = "in", SortOrder = 2)] @@ -46,6 +46,32 @@ public sealed class Arguments : TransformInputBase internal const string Summary = "Runs a previously trained predictor on the data."; + /// + /// Convenience method for creating . + /// The allows for model stacking (i.e. to combine information from multiple predictive models to generate a new model) + /// in the pipeline by using the scores from an already trained model. + /// + /// Host Environment. + /// Input . + /// The model file. + /// Role name for the features. + /// Role name for the group column. + public static IDataTransform Create(IHostEnvironment env, + IDataView input, + string inputModelFile, + string featureColumn = DefaultColumnNames.Features, + string groupColumn = DefaultColumnNames.GroupId) + { + var args = new Arguments() + { + FeatureColumn = featureColumn, + GroupColumn = groupColumn, + InputModelFile = inputModelFile + }; + + return Create(env, args, input); + } + public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input) { Contracts.CheckValue(env, nameof(env)); @@ -62,9 +88,9 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV } string feat = TrainUtils.MatchNameOrDefaultOrNull(env, input.Schema, - "featureColumn", args.FeatureColumn, DefaultColumnNames.Features); + nameof(args.FeatureColumn), args.FeatureColumn, DefaultColumnNames.Features); string group = TrainUtils.MatchNameOrDefaultOrNull(env, input.Schema, - "groupColumn", args.GroupColumn, DefaultColumnNames.GroupId); + nameof(args.GroupColumn), args.GroupColumn, DefaultColumnNames.GroupId); var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumn); return ScoreUtils.GetScorer(args.Scorer, predictor, input, feat, group, customCols, env, trainSchema); @@ -119,32 +145,90 @@ public abstract class ArgumentsBase : ArgumentsBase public sealed class Arguments : ArgumentsBase { - [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "")] - public SubComponent Calibrator = new SubComponent("PlattCalibration"); + [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "", SignatureType = typeof(SignatureCalibrator))] + public IComponentFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] public int MaxCalibrationExamples = 1000000000; - [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "")] - public SubComponent Scorer; + [Argument(ArgumentType.Multiple, HelpText = "Scorer to use", NullName = "", SignatureType = typeof(SignatureDataScorer))] + public IComponentFactory Scorer; } internal const string Summary = "Trains a predictor, or loads it from a file, and runs it on the data."; + /// + /// Convenience method for creating . + /// The allows for model stacking (i.e. to combine information from multiple predictive models to generate a new model) + /// in the pipeline by training a model first and then using the scores from the trained model. + /// + /// Unlike , the trains the model on the fly as name indicates. + /// + /// Host Environment. + /// Input . + /// The object i.e. the learning algorithm that will be used for training the model. + /// Role name for features. + /// Role name for label. + /// Role name for the group column. + public static IDataTransform Create(IHostEnvironment env, + IDataView input, + ITrainer trainer, + string featureColumn = DefaultColumnNames.Features, + string labelColumn = DefaultColumnNames.Label, + string groupColumn = DefaultColumnNames.GroupId) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(input, nameof(input)); + env.CheckValue(trainer, nameof(trainer)); + env.CheckValue(featureColumn, nameof(featureColumn)); + env.CheckValue(labelColumn, nameof(labelColumn)); + env.CheckValue(groupColumn, nameof(groupColumn)); + + var args = new Arguments() + { + FeatureColumn = featureColumn, + LabelColumn = labelColumn, + GroupColumn = groupColumn + }; + + return Create(env, args, trainer, input, null); + } + public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(args, nameof(args)); + env.CheckUserArg(args.Trainer.IsGood(), nameof(args.Trainer), + "Trainer cannot be null. If your model is already trained, please use ScoreTransform instead."); env.CheckValue(input, nameof(input)); + + return Create(env, args, args.Trainer.CreateInstance(env), input, null); + } + + public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, IComponentFactory mapperFactory) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); env.CheckUserArg(args.Trainer.IsGood(), nameof(args.Trainer), "Trainer cannot be null. If your model is already trained, please use ScoreTransform instead."); + env.CheckValue(input, nameof(input)); + env.CheckValueOrNull(mapperFactory); + + return Create(env, args, args.Trainer.CreateInstance(env), input, mapperFactory); + } + + private static IDataTransform Create(IHostEnvironment env, Arguments args, ITrainer trainer, IDataView input, IComponentFactory mapperFactory) + { + Contracts.AssertValue(env, nameof(env)); + env.AssertValue(args, nameof(args)); + env.AssertValue(trainer, nameof(trainer)); + env.AssertValue(input, nameof(input)); var host = env.Register("TrainAndScoreTransform"); using (var ch = host.Start("Train")) { ch.Trace("Constructing trainer"); - ITrainer trainer = args.Trainer.CreateInstance(host); var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumn); string feat; string group; @@ -154,7 +238,7 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV ch.Done(); - return ScoreUtils.GetScorer(args.Scorer, predictor, input, feat, group, customCols, env, data.Schema); + return ScoreUtils.GetScorer(args.Scorer, predictor, input, feat, group, customCols, env, data.Schema, mapperFactory); } } diff --git a/src/Microsoft.ML.Data/Transforms/doc.xml b/src/Microsoft.ML.Data/Transforms/doc.xml index 13f108a107..5a216580c3 100644 --- a/src/Microsoft.ML.Data/Transforms/doc.xml +++ b/src/Microsoft.ML.Data/Transforms/doc.xml @@ -9,10 +9,10 @@ This transform removes the entire row if any of the input columns have a missing value in that row. This preprocessing is required for many ML algorithms that cannot work with missing values. Useful if any missing entry invalidates the entire row. - If the is set to true, this transform would do the exact opposite, + If the is set to true, this transform would do the exact opposite, it will keep only the rows that have missing values. - + @@ -44,7 +44,7 @@ pipeline.Add(new TextToKeyConverter(("Column", "OutColumn")) { - Sort = TermTransformSortOrder.Occurrence + Sort = TermTransformSortOrder.Occurrence }); @@ -82,7 +82,7 @@ It can be changed to true for known length vectors, but it results in an error if changed to false for variable length vectors. - + @@ -90,7 +90,7 @@ pipeline.Add(new MissingValueHandler("FeatureCol", "CleanFeatureCol") { - ReplaceWith = NAHandleTransformReplacementKind.Mean + ReplaceWith = NAHandleTransformReplacementKind.Mean }); diff --git a/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj b/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj index ddd4557788..ef0ad01a40 100644 --- a/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj +++ b/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj @@ -10,6 +10,7 @@ + diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs index a5c9c757a4..9b9900a65f 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs @@ -9,7 +9,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners { - public abstract class BaseScalarStacking : BaseStacking + public abstract class BaseScalarStacking : BaseStacking { internal BaseScalarStacking(IHostEnvironment env, string name, ArgumentsBase args) : base(env, name, args) diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs index f30174a31d..c294a1fcde 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs @@ -7,6 +7,7 @@ using System.Threading.Tasks; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; @@ -15,7 +16,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners { using ColumnRole = RoleMappedSchema.ColumnRole; - public abstract class BaseStacking : IStackingTrainer + public abstract class BaseStacking : IStackingTrainer { public abstract class ArgumentsBase { @@ -24,13 +25,10 @@ public abstract class ArgumentsBase [TGUI(Label = "Validation Dataset Proportion")] public Single ValidationDatasetProportion = 0.3f; - [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, - Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] - [TGUI(Label = "Base predictor")] - public SubComponent>, TSigBase> BasePredictorType; + internal abstract IComponentFactory>> GetPredictorFactory(); } - protected readonly SubComponent>, TSigBase> BasePredictorType; + protected readonly IComponentFactory>> BasePredictorType; protected readonly IHost Host; protected IPredictorProducing Meta; @@ -45,10 +43,10 @@ internal BaseStacking(IHostEnvironment env, string name, ArgumentsBase args) Host.CheckUserArg(0 <= args.ValidationDatasetProportion && args.ValidationDatasetProportion < 1, nameof(args.ValidationDatasetProportion), "The validation proportion for stacking should be greater than or equal to 0 and less than 1"); - Host.CheckUserArg(args.BasePredictorType.IsGood(), nameof(args.BasePredictorType)); ValidationDatasetProportion = args.ValidationDatasetProportion; - BasePredictorType = args.BasePredictorType; + BasePredictorType = args.GetPredictorFactory(); + Host.CheckValue(BasePredictorType, nameof(BasePredictorType)); } internal BaseStacking(IHostEnvironment env, string name, ModelLoadContext ctx) @@ -187,7 +185,7 @@ public void Train(List>> models, var view = bldr.GetDataView(); var rmd = new RoleMappedData(view, DefaultColumnNames.Label, DefaultColumnNames.Features); - var trainer = BasePredictorType.CreateInstance(host); + var trainer = BasePredictorType.CreateComponent(host); if (trainer.Info.NeedNormalization) ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this."); Meta = trainer.Train(rmd); diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs index 2ef74c8169..40d59b631a 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs @@ -8,7 +8,10 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Learners; using Microsoft.ML.Runtime.Model; [assembly: LoadableClass(typeof(MultiStacking), typeof(MultiStacking.Arguments), typeof(SignatureCombiner), @@ -20,7 +23,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners { using TVectorPredictor = IPredictorProducing>; - public sealed class MultiStacking : BaseStacking, SignatureMultiClassClassifierTrainer>, ICanSaveModel, IMultiClassOutputCombiner + public sealed class MultiStacking : BaseStacking>, ICanSaveModel, IMultiClassOutputCombiner { public const string LoadName = "MultiStacking"; public const string LoaderSignature = "MultiStackingCombiner"; @@ -38,13 +41,24 @@ private static VersionInfo GetVersionInfo() [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory { + [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, + Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMultiClassClassifierTrainer))] + [TGUI(Label = "Base predictor")] + public IComponentFactory> BasePredictorType; + + internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiStacking(env, this); public Arguments() { // REVIEW: Perhaps we can have a better non-parametetric learner. - BasePredictorType = new SubComponent, SignatureMultiClassClassifierTrainer>( - "OVA", "p=FastTreeBinaryClassification"); + BasePredictorType = ComponentFactoryUtils.CreateFromFunction( + env => new Ova(env, new Ova.Arguments() + { + PredictorType = ComponentFactoryUtils.CreateFromFunction( + e => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments())) + })); } } diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs index 0b5f8e6057..436e365b79 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs @@ -7,6 +7,8 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Model; [assembly: LoadableClass(typeof(RegressionStacking), typeof(RegressionStacking.Arguments), typeof(SignatureCombiner), @@ -19,7 +21,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners { using TScalarPredictor = IPredictorProducing; - public sealed class RegressionStacking : BaseScalarStacking, IRegressionOutputCombiner, ICanSaveModel + public sealed class RegressionStacking : BaseScalarStacking, IRegressionOutputCombiner, ICanSaveModel { public const string LoadName = "RegressionStacking"; public const string LoaderSignature = "RegressionStacking"; @@ -37,9 +39,17 @@ private static VersionInfo GetVersionInfo() [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] public sealed class Arguments : ArgumentsBase, ISupportRegressionOutputCombinerFactory { + [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, + Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))] + [TGUI(Label = "Base predictor")] + public IComponentFactory> BasePredictorType; + + internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + public Arguments() { - BasePredictorType = new SubComponent, SignatureRegressorTrainer>("FastTreeRegression"); + BasePredictorType = ComponentFactoryUtils.CreateFromFunction( + env => new FastTreeRegressionTrainer(env, new FastTreeRegressionTrainer.Arguments())); } public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new RegressionStacking(env, this); diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs index f3481e9936..f0ced9d947 100644 --- a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs @@ -5,9 +5,10 @@ using System; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; -using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Model; [assembly: LoadableClass(typeof(Stacking), typeof(Stacking.Arguments), typeof(SignatureCombiner), Stacking.UserName, Stacking.LoadName)] @@ -16,7 +17,7 @@ namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners { using TScalarPredictor = IPredictorProducing; - public sealed class Stacking : BaseScalarStacking, IBinaryOutputCombiner, ICanSaveModel + public sealed class Stacking : BaseScalarStacking, IBinaryOutputCombiner, ICanSaveModel { public const string UserName = "Stacking"; public const string LoadName = "Stacking"; @@ -35,9 +36,17 @@ private static VersionInfo GetVersionInfo() [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] public sealed class Arguments : ArgumentsBase, ISupportBinaryOutputCombinerFactory { + [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, + Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))] + [TGUI(Label = "Base predictor")] + public IComponentFactory> BasePredictorType; + + internal override IComponentFactory> GetPredictorFactory() => BasePredictorType; + public Arguments() { - BasePredictorType = new SubComponent, SignatureBinaryClassifierTrainer>("FastTreeBinaryClassification"); + BasePredictorType = ComponentFactoryUtils.CreateFromFunction( + env => new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments())); } public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new Stacking(env, this); diff --git a/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs index 3ac78ed91e..e4d6cb0d9e 100644 --- a/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs +++ b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs @@ -67,7 +67,7 @@ public BoundBase(SchemaBindablePipelineEnsembleBase parent, RoleMappedSchema sch Parent.PredictorModels[i].PrepareData(Parent.Host, emptyDv, out RoleMappedData rmd, out IPredictor predictor); // Get the predictor as a bindable mapper, and bind it to the RoleMappedSchema found above. - var bindable = ScoreUtils.GetSchemaBindableMapper(Parent.Host, Parent.PredictorModels[i].Predictor, null); + var bindable = ScoreUtils.GetSchemaBindableMapper(Parent.Host, Parent.PredictorModels[i].Predictor); Mappers[i] = bindable.Bind(Parent.Host, rmd.Schema) as ISchemaBoundRowMapper; if (Mappers[i] == null) throw Parent.Host.Except("Predictor {0} is not a row to row mapper", i); diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs index 8465e1a5e8..513ad95139 100644 --- a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs @@ -54,16 +54,16 @@ public virtual IList>> Prune(ILi return models; } - private SubComponent GetEvaluatorSubComponent() + private IEvaluator GetEvaluator(IHostEnvironment env) { switch (PredictionKind) { case PredictionKind.BinaryClassification: - return new SubComponent(BinaryClassifierEvaluator.LoadName); + return new BinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments()); case PredictionKind.Regression: - return new SubComponent(RegressionEvaluator.LoadName); + return new RegressionEvaluator(env, new RegressionEvaluator.Arguments()); case PredictionKind.MultiClassClassification: - return new SubComponent(MultiClassClassifierEvaluator.LoadName); + return new MultiClassClassifierEvaluator(env, new MultiClassClassifierEvaluator.Arguments()); default: throw Host.Except("Unrecognized prediction kind '{0}'", PredictionKind); } @@ -81,12 +81,11 @@ public virtual void CalculateMetrics(FeatureSubsetModel public sealed class EnsembleTrainer : EnsembleTrainerBase, + IBinarySubModelSelector, IBinaryOutputCombiner>, IModelCombiner { public const string LoadNameValue = "WeightedEnsemble"; @@ -44,9 +45,18 @@ public sealed class Arguments : ArgumentsBase [TGUI(Label = "Output combiner", Description = "Output combiner type")] public ISupportBinaryOutputCombinerFactory OutputCombiner = new MedianFactory(); + [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureBinaryClassifierTrainer))] + public IComponentFactory>[] BasePredictors; + + internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + public Arguments() { - BasePredictors = new[] { new SubComponent, SignatureBinaryClassifierTrainer>("LinearSVM") }; + BasePredictors = new[] + { + ComponentFactoryUtils.CreateFromFunction( + env => new LinearSvm(env, new LinearSvm.Arguments())) + }; } } diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs index 0a350ef8ee..5ebf5cfac0 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs @@ -20,7 +20,7 @@ namespace Microsoft.ML.Runtime.Ensemble { using Stopwatch = System.Diagnostics.Stopwatch; - public abstract class EnsembleTrainerBase : TrainerBase + public abstract class EnsembleTrainerBase : TrainerBase where TPredictor : class, IPredictorProducing where TSelector : class, ISubModelSelector where TCombiner : class, IOutputCombiner @@ -53,8 +53,7 @@ public abstract class ArgumentsBase : LearnerInputBaseWithLabel [TGUI(Label = "Show Sub-Model Metrics")] public bool ShowMetrics; - [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] - public SubComponent>, TSig>[] BasePredictors; + internal abstract IComponentFactory>>[] GetPredictorFactories(); } private const int DefaultNumModels = 50; @@ -78,21 +77,22 @@ private protected EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, using (var ch = Host.Start("Init")) { - ch.CheckUserArg(Utils.Size(Args.BasePredictors) > 0, nameof(Args.BasePredictors), "This should have at-least one value"); + var predictorFactories = Args.GetPredictorFactories(); + ch.CheckUserArg(Utils.Size(predictorFactories) > 0, nameof(EnsembleTrainer.Arguments.BasePredictors), "This should have at-least one value"); NumModels = Args.NumModels ?? - (Args.BasePredictors.Length == 1 ? DefaultNumModels : Args.BasePredictors.Length); + (predictorFactories.Length == 1 ? DefaultNumModels : predictorFactories.Length); ch.CheckUserArg(NumModels > 0, nameof(Args.NumModels), "Must be positive, or null to indicate numModels is the number of base predictors"); - if (Utils.Size(Args.BasePredictors) > NumModels) + if (Utils.Size(predictorFactories) > NumModels) ch.Warning("The base predictor count is greater than models count. Some of the base predictors will be ignored."); _subsetSelector = Args.SamplingType.CreateComponent(Host); Trainers = new ITrainer>[NumModels]; for (int i = 0; i < Trainers.Length; i++) - Trainers[i] = Args.BasePredictors[i % Args.BasePredictors.Length].CreateInstance(Host); + Trainers[i] = predictorFactories[i % predictorFactories.Length].CreateComponent(Host); // We infer normalization and calibration preferences from the trainers. However, even if the internal trainers // don't need caching we are performing multiple passes over the data, so it is probably appropriate to always cache. Info = new TrainerInfo( diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs index 4421cd5838..d5c60e24a6 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs @@ -12,7 +12,9 @@ using Microsoft.ML.Runtime.Ensemble; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Learners; [assembly: LoadableClass(MulticlassDataPartitionEnsembleTrainer.Summary, typeof(MulticlassDataPartitionEnsembleTrainer), typeof(MulticlassDataPartitionEnsembleTrainer.Arguments), @@ -28,7 +30,7 @@ namespace Microsoft.ML.Runtime.Ensemble /// public sealed class MulticlassDataPartitionEnsembleTrainer : EnsembleTrainerBase, EnsembleMultiClassPredictor, - IMulticlassSubModelSelector, IMultiClassOutputCombiner, SignatureMultiClassClassifierTrainer>, + IMulticlassSubModelSelector, IMultiClassOutputCombiner>, IModelCombiner { public const string LoadNameValue = "WeightedEnsembleMulticlass"; @@ -45,9 +47,18 @@ public sealed class Arguments : ArgumentsBase [TGUI(Label = "Output combiner", Description = "Output combiner type")] public ISupportMulticlassOutputCombinerFactory OutputCombiner = new MultiMedian.Arguments(); + [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureMultiClassClassifierTrainer))] + public IComponentFactory>[] BasePredictors; + + internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + public Arguments() { - BasePredictors = new[] { new SubComponent, SignatureMultiClassClassifierTrainer>("MultiClassLogisticRegression") }; + BasePredictors = new[] + { + ComponentFactoryUtils.CreateFromFunction( + env => new MulticlassLogisticRegression(env, new MulticlassLogisticRegression.Arguments())) + }; } } diff --git a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs index 1cc36f20cd..3fe853aff5 100644 --- a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs +++ b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs @@ -12,7 +12,9 @@ using Microsoft.ML.Runtime.Ensemble; using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Learners; [assembly: LoadableClass(typeof(RegressionEnsembleTrainer), typeof(RegressionEnsembleTrainer.Arguments), new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer) }, @@ -23,7 +25,7 @@ namespace Microsoft.ML.Runtime.Ensemble { using TScalarPredictor = IPredictorProducing; public sealed class RegressionEnsembleTrainer : EnsembleTrainerBase, + IRegressionSubModelSelector, IRegressionOutputCombiner>, IModelCombiner { public const string LoadNameValue = "EnsembleRegression"; @@ -39,9 +41,18 @@ public sealed class Arguments : ArgumentsBase [TGUI(Label = "Output combiner", Description = "Output combiner type")] public ISupportRegressionOutputCombinerFactory OutputCombiner = new MedianFactory(); + [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureRegressorTrainer))] + public IComponentFactory>[] BasePredictors; + + internal override IComponentFactory>[] GetPredictorFactories() => BasePredictors; + public Arguments() { - BasePredictors = new[] { new SubComponent, SignatureRegressorTrainer>("OnlineGradientDescent") }; + BasePredictors = new[] + { + ComponentFactoryUtils.CreateFromFunction( + env => new OnlineGradientDescentTrainer(env, new OnlineGradientDescentTrainer.Arguments())) + }; } } diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs index fad40495f4..9f24b4bc09 100644 --- a/src/Microsoft.ML.FastTree/FastTree.cs +++ b/src/Microsoft.ML.FastTree/FastTree.cs @@ -95,7 +95,7 @@ private protected FastTreeTrainerBase(IHostEnvironment env, TArgs args) // The discretization step renders this trainer non-parametric, and therefore it does not need normalization. // Also since it builds its own internal discretized columnar structures, it cannot benefit from caching. // Finally, even the binary classifiers, being logitboost, tend to not benefit from external calibration. - Info = new TrainerInfo(normalization: false, caching: false, calibration: NeedCalibration); + Info = new TrainerInfo(normalization: false, caching: false, calibration: NeedCalibration, supportValid: true); int numThreads = Args.NumThreads ?? Environment.ProcessorCount; if (Host.ConcurrencyFactor > 0 && numThreads > Host.ConcurrencyFactor) { diff --git a/src/Microsoft.ML.FastTree/FastTreeArguments.cs b/src/Microsoft.ML.FastTree/FastTreeArguments.cs index 2c6fc02745..ffc12b9540 100644 --- a/src/Microsoft.ML.FastTree/FastTreeArguments.cs +++ b/src/Microsoft.ML.FastTree/FastTreeArguments.cs @@ -228,14 +228,14 @@ public abstract class TreeArgs : LearnerInputBaseWithGroupId // REVIEW: Different from original FastRank arguments (shortname l vs. nl). Different default from TLC FR Wrapper (20 vs. 20). [Argument(ArgumentType.LastOccurenceWins, HelpText = "The max number of leaves in each regression tree", ShortName = "nl", SortOrder = 2)] [TGUI(Description = "The maximum number of leaves per tree", SuggestedSweeps = "2-128;log;inc:4")] - [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, isLogScale:true, stepSize:4)] + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, isLogScale: true, stepSize: 4)] public int NumLeaves = 20; // REVIEW: Arrays not supported in GUI // REVIEW: Different shortname than FastRank module. Same as the TLC FRWrapper. [Argument(ArgumentType.LastOccurenceWins, HelpText = "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", ShortName = "mil", SortOrder = 3)] [TGUI(Description = "Minimum number of training instances required to form a leaf", SuggestedSweeps = "1,10,50")] - [TlcModule.SweepableDiscreteParamAttribute("MinDocumentsInLeafs", new object[] {1, 10, 50})] + [TlcModule.SweepableDiscreteParamAttribute("MinDocumentsInLeafs", new object[] { 1, 10, 50 })] public int MinDocumentsInLeafs = 10; // REVIEW: Different shortname than FastRank module. Same as the TLC FRWrapper. @@ -364,17 +364,17 @@ public enum OptimizationAlgorithmType { GradientDescent, AcceleratedGradientDesc [Argument(ArgumentType.LastOccurenceWins, HelpText = "The learning rate", ShortName = "lr", SortOrder = 4)] [TGUI(Label = "Learning Rate", SuggestedSweeps = "0.025-0.4;log")] - [TlcModule.SweepableFloatParamAttribute("LearningRates", 0.025f, 0.4f, isLogScale:true)] + [TlcModule.SweepableFloatParamAttribute("LearningRates", 0.025f, 0.4f, isLogScale: true)] public Double LearningRates = 0.2; [Argument(ArgumentType.AtMostOnce, HelpText = "Shrinkage", ShortName = "shrk")] [TGUI(Label = "Shrinkage", SuggestedSweeps = "0.25-4;log")] - [TlcModule.SweepableFloatParamAttribute("Shrinkage", 0.025f, 4f, isLogScale:true)] + [TlcModule.SweepableFloatParamAttribute("Shrinkage", 0.025f, 4f, isLogScale: true)] public Double Shrinkage = 1; [Argument(ArgumentType.AtMostOnce, HelpText = "Dropout rate for tree regularization", ShortName = "tdrop")] [TGUI(SuggestedSweeps = "0,0.000000001,0.05,0.1,0.2")] - [TlcModule.SweepableDiscreteParamAttribute("DropoutRate", new object[] { 0.0f, 1E-9f, 0.05f, 0.1f, 0.2f})] + [TlcModule.SweepableDiscreteParamAttribute("DropoutRate", new object[] { 0.0f, 1E-9f, 0.05f, 0.1f, 0.2f })] public Double DropoutRate = 0; [Argument(ArgumentType.AtMostOnce, HelpText = "Sample each query 1 in k times in the GetDerivatives function", ShortName = "sr")] diff --git a/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs b/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs index f404f3ae95..e9f5f326f6 100644 --- a/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs +++ b/src/Microsoft.ML.FastTree/TreeEnsembleFeaturizer.cs @@ -666,20 +666,21 @@ public static IDataTransform Create(IHostEnvironment env, Arguments args, IDataV ch.Assert(args.Trainer.IsGood()); ch.Trace("Creating TrainAndScoreTransform"); - string scorerSettings = CmdParser.GetSettings(ch, scorerArgs, - new TreeEnsembleFeaturizerBindableMapper.Arguments()); - var scorer = - new SubComponent( - TreeEnsembleFeaturizerBindableMapper.LoadNameShort, scorerSettings); var trainScoreArgs = new TrainAndScoreTransform.Arguments(); args.CopyTo(trainScoreArgs); trainScoreArgs.Trainer = new SubComponent(args.Trainer.Kind, args.Trainer.Settings); + trainScoreArgs.Scorer = ComponentFactoryUtils.CreateFromFunction( + (e, data, mapper, trainSchema) => Create(e, scorerArgs, data, mapper, trainSchema)); + + var mapperFactory = ComponentFactoryUtils.CreateFromFunction( + (e, predictor) => new TreeEnsembleFeaturizerBindableMapper(e, scorerArgs, predictor)); + var labelInput = AppendLabelTransform(host, ch, input, trainScoreArgs.LabelColumn, args.LabelPermutationSeed); - trainScoreArgs.Scorer = scorer; - var scoreXf = TrainAndScoreTransform.Create(host, trainScoreArgs, labelInput); + var scoreXf = TrainAndScoreTransform.Create(host, trainScoreArgs, labelInput, mapperFactory); + if (input == labelInput) return scoreXf; return (IDataTransform)ApplyTransformUtils.ApplyAllTransformsToData(host, scoreXf, input, labelInput); diff --git a/src/Microsoft.ML.FastTree/doc.xml b/src/Microsoft.ML.FastTree/doc.xml index 26d3c8c129..cd32b94ebd 100644 --- a/src/Microsoft.ML.FastTree/doc.xml +++ b/src/Microsoft.ML.FastTree/doc.xml @@ -29,7 +29,7 @@ In case of a ranking problem, the instances are ordered by the output value of the ensemble. For more information see: - + Wikipedia: Gradient boosting (Gradient tree boosting). Greedy function approximation: A gradient boosting machine. @@ -144,16 +144,7 @@ - Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector - to three outputs: - - A vector containing the individual tree outputs of the tree ensemble. - A vector indicating the leaves that the feature vector falls on in the tree ensemble. - A vector indicating the paths that the feature vector falls on in the tree ensemble. - - If a both a model file and a trainer are specified - will use the model file. If neither are specified, - will train a default FastTree model. - This can handle key labels by training a regression model towards their optionally permuted indices. + Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to outputs. In machine learning​ it is a pretty common and powerful approach to utilize the already trained model in the process of defining features. @@ -169,7 +160,18 @@ The weights of the linear model are often used as a crude indicator of 'feature importance'. At the very minimum, the 0-weight features are not needed by the model, and there's no reason to compute them. - Tree featurizer uses the decision tree ensembles for feature engineering in the same fashion as above. + + Tree featurizer uses the decision tree ensembles for feature engineering in the same fashion as above. + It trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: + + + A vector containing the individual tree outputs of the tree ensemble. + A vector indicating the leaves that the feature vector falls on in the tree ensemble. + A vector indicating the paths that the feature vector falls on in the tree ensemble. + + If a both a model file and a trainer are specified - will use the model file. If neither are specified, + will train a default FastTree model. + This can handle key labels by training a regression model towards their optionally permuted indices. Let's assume that we've built a tree ensemble of 100 trees with 100 leaves each (it doesn't matter whether boosting was used or not in training). If we associate each leaf of each tree with a sequential integer, we can, for every incoming example x, produce an indicator vector L(x), where Li(x) = 1 if the example x 'falls' into the leaf #i, and 0 otherwise. diff --git a/src/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.csproj b/src/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.csproj new file mode 100644 index 0000000000..39f2db5c80 --- /dev/null +++ b/src/Microsoft.ML.HalLearners/Microsoft.ML.HalLearners.csproj @@ -0,0 +1,17 @@ + + + + netstandard2.0 + Microsoft.ML.HalLearners + true + + + + + + + + + + + diff --git a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs b/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs similarity index 95% rename from src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs rename to src/Microsoft.ML.HalLearners/OlsLinearRegression.cs index 7f47271f68..dbf2999657 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs +++ b/src/Microsoft.ML.HalLearners/OlsLinearRegression.cs @@ -7,14 +7,15 @@ using System; using System.Collections.Generic; using System.IO; -using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.HalLearners; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Learners; using Microsoft.ML.Runtime.Model; -using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Training; using System.Runtime.InteropServices; @@ -28,8 +29,11 @@ "OLS Linear Regression Executor", OlsLinearRegressionPredictor.LoaderSignature)] -namespace Microsoft.ML.Runtime.Learners +[assembly: LoadableClass(typeof(void), typeof(OlsLinearRegressionTrainer), null, typeof(SignatureEntryPointModule), OlsLinearRegressionTrainer.LoadNameValue)] + +namespace Microsoft.ML.Runtime.HalLearners { + /// public sealed class OlsLinearRegressionTrainer : TrainerBase { public sealed class Arguments : LearnerInputBaseWithWeight @@ -51,11 +55,6 @@ public sealed class Arguments : LearnerInputBaseWithWeight public const string ShortName = "ols"; internal const string Summary = "The ordinary least square regression fits the target function as a linear function of the numerical features " + "that minimizes the square loss function."; - internal const string Remarks = @" -Ordinary least squares (OLS) is a parameterized regression method. -It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables. -By minimizing the squares of the difference between observed values and the predictions, the parameters of the regressor can be estimated. -"; private readonly Float _l2Weight; private readonly bool _perParameterSignificance; @@ -222,7 +221,7 @@ private OlsLinearRegressionPredictor TrainCore(IChannel ch, FloatLabelCursor.Fac catch (DllNotFoundException) { // REVIEW: Is there no better way? - throw ch.ExceptNotSupp("The MKL library (Microsoft.ML.MklImports.dll) or one of its dependencies is missing."); + throw ch.ExceptNotSupp("The MKL library (libMklImports) or one of its dependencies is missing."); } // Solve for beta in (LL')beta = X'y: Mkl.Pptrs(Mkl.Layout.RowMajor, Mkl.UpLo.Lo, m, 1, xtx, xty, 1); @@ -331,7 +330,7 @@ private OlsLinearRegressionPredictor TrainCore(IChannel ch, FloatLabelCursor.Fac internal static class Mkl { - private const string DllName = "Microsoft.ML.MklImports.dll"; + private const string DllName = "MklImports"; public enum Layout { @@ -463,6 +462,24 @@ public static void Pptri(Layout layout, UpLo uplo, int n, Double[] ap) } } } + + [TlcModule.EntryPoint(Name = "Trainers.OrdinaryLeastSquaresRegressor", + Desc = "Train an OLS regression model.", + UserName = UserNameValue, + ShortName = ShortName, + XmlInclude = new[] { @"" })] + public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainOLS"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new OlsLinearRegressionTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); + } } /// diff --git a/src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs b/src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs new file mode 100644 index 0000000000..29f9d88bbf --- /dev/null +++ b/src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs @@ -0,0 +1,841 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Float = System.Single; + +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using System.Security; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.Conversion; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Calibration; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.SymSgd; +using Microsoft.ML.Runtime.Training; + +[assembly: LoadableClass(typeof(SymSgdClassificationTrainer), typeof(SymSgdClassificationTrainer.Arguments), + new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) }, + SymSgdClassificationTrainer.UserNameValue, + SymSgdClassificationTrainer.LoadNameValue, + SymSgdClassificationTrainer.ShortName)] + +[assembly: LoadableClass(typeof(void), typeof(SymSgdClassificationTrainer), null, typeof(SignatureEntryPointModule), "SymSGD")] + +namespace Microsoft.ML.Runtime.SymSgd +{ + using TPredictor = IPredictorWithFeatureWeights; + + /// + public sealed class SymSgdClassificationTrainer : + TrainerBase, + ITrainer + { + public const string LoadNameValue = "SymbolicSGD"; + public const string UserNameValue = "Symbolic SGD (binary)"; + public const string ShortName = "SymSGD"; + + public sealed class Arguments : LearnerInputBaseWithLabel + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Degree of lock-free parallelism. Determinism not guaranteed. " + + "Multi-threading is not supported currently.", ShortName = "nt")] + public int? NumberOfThreads; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of passes over the data.", ShortName = "iter", SortOrder = 50)] + [TGUI(SuggestedSweeps = "1,5,10,20,30,40,50")] + [TlcModule.SweepableDiscreteParam("NumberOfIterations", new object[] { 1, 5, 10, 20, 30, 40, 50 })] + public int NumberOfIterations = 50; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Tolerance for difference in average loss in consecutive passes.", ShortName = "tol")] + public float Tolerance = 1e-4f; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Learning rate", ShortName = "lr", NullName = "", SortOrder = 51)] + [TGUI(SuggestedSweeps = ",1e1,1e0,1e-1,1e-2,1e-3")] + [TlcModule.SweepableDiscreteParam("LearningRate", new object[] { "", 1e1f, 1e0f, 1e-1f, 1e-2f, 1e-3f })] + public float? LearningRate; + + [Argument(ArgumentType.AtMostOnce, HelpText = "L2 regularization", ShortName = "l2", SortOrder = 52)] + [TGUI(SuggestedSweeps = "0.0,1e-5,1e-5,1e-6,1e-7")] + [TlcModule.SweepableDiscreteParam("L2Regularization", new object[] { 0.0f, 1e-5f, 1e-5f, 1e-6f, 1e-7f })] + public float L2Regularization; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of iterations each thread learns a local model until combining it with the " + + "global model. Low value means more updated global model and high value means less cache traffic.", ShortName = "freq", NullName = "")] + [TGUI(SuggestedSweeps = ",5,20")] + [TlcModule.SweepableDiscreteParam("UpdateFrequency", new object[] { "", 5, 20 })] + public int? UpdateFrequency; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The acceleration memory budget in MB", ShortName = "accelMemBudget")] + public long MemorySize = 1024; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Shuffle data?", ShortName = "shuf")] + public bool Shuffle = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")] + public Float PositiveInstanceWeight = 1; + + public void Check(IExceptionContext ectx) + { + ectx.CheckUserArg(LearningRate == null || LearningRate.Value > 0, nameof(LearningRate), "Must be positive."); + ectx.CheckUserArg(NumberOfIterations > 0, nameof(NumberOfIterations), "Must be positive."); + ectx.CheckUserArg(PositiveInstanceWeight > 0, nameof(PositiveInstanceWeight), "Must be positive"); + ectx.CheckUserArg(UpdateFrequency == null || UpdateFrequency > 0, nameof(UpdateFrequency), "Must be positive"); + } + } + + public override TrainerInfo Info { get; } + private readonly Arguments _args; + + /// + /// This method ensures that the data meets the requirements of this trainer and its + /// subclasses, injects necessary transforms, and throws if it couldn't meet them. + /// + /// The channel + /// The training examples + /// Gets the length of weights and bias array. For binary classification and regression, + /// this is 1. For multi-class classification, this equals the number of classes on the label. + /// A potentially modified version of + private RoleMappedData PrepareDataFromTrainingExamples(IChannel ch, RoleMappedData examples, out int weightSetCount) + { + ch.AssertValue(examples); + CheckLabel(examples, out weightSetCount); + examples.CheckFeatureFloatVector(); + var idvToShuffle = examples.Data; + IDataView idvToFeedTrain; + if (idvToShuffle.CanShuffle) + idvToFeedTrain = idvToShuffle; + else + { + var shuffleArgs = new ShuffleTransform.Arguments + { + PoolOnly = false, + ForceShuffle = _args.Shuffle + }; + idvToFeedTrain = new ShuffleTransform(Host, shuffleArgs, idvToShuffle); + } + + ch.Assert(idvToFeedTrain.CanShuffle); + + var roles = examples.Schema.GetColumnRoleNames(); + var examplesToFeedTrain = new RoleMappedData(idvToFeedTrain, roles); + + ch.AssertValue(examplesToFeedTrain.Schema.Label); + ch.AssertValue(examplesToFeedTrain.Schema.Feature); + if (examples.Schema.Weight != null) + ch.AssertValue(examplesToFeedTrain.Schema.Weight); + + int numFeatures = examplesToFeedTrain.Schema.Feature.Type.VectorSize; + ch.Check(numFeatures > 0, "Training set has no features, aborting training."); + return examplesToFeedTrain; + } + + public override TPredictor Train(TrainContext context) + { + Host.CheckValue(context, nameof(context)); + TPredictor pred; + using (var ch = Host.Start("Training")) + { + var preparedData = PrepareDataFromTrainingExamples(ch, context.TrainingSet, out int weightSetCount); + var initPred = context.InitialPredictor; + var linInitPred = (initPred as CalibratedPredictorBase)?.SubPredictor as LinearPredictor; + linInitPred = linInitPred ?? initPred as LinearPredictor; + Host.CheckParam(context.InitialPredictor == null || linInitPred != null, nameof(context), + "Initial predictor was not a linear predictor."); + pred = TrainCore(ch, preparedData, linInitPred, weightSetCount); + ch.Done(); + } + return pred; + } + + public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + + public SymSgdClassificationTrainer(IHostEnvironment env, Arguments args) + : base(env, LoadNameValue) + { + args.Check(Host); + _args = args; + Info = new TrainerInfo(); + } + + private TPredictor CreatePredictor(VBuffer weights, Float bias) + { + Host.CheckParam(weights.Length > 0, nameof(weights)); + + VBuffer maybeSparseWeights = default; + VBufferUtils.CreateMaybeSparseCopy(ref weights, ref maybeSparseWeights, + Conversions.Instance.GetIsDefaultPredicate(NumberType.Float)); + var predictor = new LinearBinaryPredictor(Host, ref maybeSparseWeights, bias); + return new ParameterMixingCalibratedPredictor(Host, predictor, new PlattCalibrator(Host, -1, 0)); + } + + [TlcModule.EntryPoint(Name = "Trainers.SymSgdBinaryClassifier", + Desc = "Train a symbolic SGD.", + UserName = SymSgdClassificationTrainer.UserNameValue, + ShortName = SymSgdClassificationTrainer.ShortName, + XmlInclude = new[] { @"" })] + public static CommonOutputs.BinaryClassificationOutput TrainSymSgd(IHostEnvironment env, Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainSymSGD"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new SymSgdClassificationTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); + } + + // We buffer instances from the cursor (limited to memorySize) and passes that buffer to + // the native code to learn for multiple instances by one interop call. + + /// + /// This struct holds the information about the size, label and isDense of each instance + /// to be able to pass it to the native code. + /// + private struct InstanceProperties + { + public readonly int FeatureCount; + public readonly float Label; + public readonly bool IsDense; + + public InstanceProperties(int featureCount, float label, bool isDense) + { + FeatureCount = featureCount; + Label = label; + IsDense = isDense; + } + } + + /// + /// ArrayManager stores multiple arrays of type in a "very long" array whose size is specified by accelChunkSize. + /// Once one of the very long arrays is full, another one is allocated to store additional arrays. The required memory + /// for this buffering is limited by memorySize. + /// + /// Note that these very long arrays can be reused. This means that learning can be done in batches without the overhead associated + /// with allocation. + /// + /// The benefit of this way of storage is that only a handful of new calls will be needed + /// which saves time. + /// + /// The type of arrays to be stored + private sealed class ArrayManager : IDisposable + { + /// + /// This structure is used for pinning very long arrays to stop GC from moving them. + /// The reason for this design is that when these arrays are passed to native code, + /// GC does not move the objects. + /// + private struct VeryLongArray + { + public T[] Buffer; + public GCHandle GcHandle; + public int Length => Buffer.Length; + public VeryLongArray(int veryLongArrayLength) + { + Buffer = new T[veryLongArrayLength]; + GcHandle = GCHandle.Alloc(Buffer, GCHandleType.Pinned); + } + + public void Free() + { + GcHandle.Free(); + } + } + // This list holds very long arrays. + private readonly List _storage; + // Length of each very long array + // This is not readonly because there might be an instance where the length of the + // instance is longer than _veryLongArrayLength and we have to adjust it + private int _veryLongArrayLength; + // This index is used to walk over _storage list. During storing or giving an array, + // we are at _storage[_storageIndex]. + private int _storageIndex; + // This index is used within a very long array from _storage[_storageIndex]. During storing or + // giving an array, we are at _storage[_storageIndex][_indexInCurArray]. + private int _indexInCurArray; + // This is used to access AccelMemBudget, AccelChunkSize and UsedMemory + private readonly SymSgdClassificationTrainer _trainer; + + private readonly IChannel _ch; + + // Size of type T + private readonly int _sizeofT; + + /// + /// Constructor for initializing _storage and other indices. + /// + /// + /// + public ArrayManager(SymSgdClassificationTrainer trainer, IChannel ch) + { + _storage = new List(); + // Setting the default value to 2^17. + _veryLongArrayLength = (1 << 17); + _indexInCurArray = 0; + _storageIndex = 0; + _trainer = trainer; + _ch = ch; + _sizeofT = Marshal.SizeOf(typeof(T)); + } + + /// + /// + /// Returns if the allocation was successful + private bool CheckAndAllocate() + { + // Check if this allocation violates the memorySize. + if (_trainer.UsedMemory + _veryLongArrayLength * _sizeofT <= _trainer.AcceleratedMemoryBudgetBytes) + { + // Add the additional allocation to UsedMemory + _trainer.UsedMemory += _veryLongArrayLength * _sizeofT; + _storage.Add(new VeryLongArray(_veryLongArrayLength)); + return true; + } + // If allocation violates the budget, bail. + return false; + } + + /// + /// This method checks if an array of size fits in _storage[_storageIndex][_indexInCurArray.._indexInCurArray+size-1]. + /// + /// The size of the array to fit in the very long array _storage[_storageIndex] + /// + private bool FitsInCurArray(int size) + { + _ch.Assert(_storage[_storageIndex].Length == _veryLongArrayLength); + return _indexInCurArray <= _veryLongArrayLength - size; + } + + /// + /// Tries to add array to the storage without violating the restriction of memorySize. + /// + /// The array to be added + /// Length of the array. .Length is unreliable since TLC cursoring + /// has its own allocation mechanism. + /// Return if the allocation was successful + public bool AddToStorage(T[] instArray, int instArrayLength) + { + _ch.Assert(0 < instArrayLength && instArrayLength <= Utils.Size(instArray)); + _ch.Assert(instArrayLength * _sizeofT * 2 < _trainer.AcceleratedMemoryBudgetBytes); + if (instArrayLength > _veryLongArrayLength) + { + // In this case, we need to increase _veryLongArrayLength. + if (_indexInCurArray == 0 && _storageIndex == 0) + { + // If there are no instances loaded, all of the allocated very long arrays need to be deallocated + // and longer _veryLongArrayLength be used instead. + DeallocateVeryLongArrays(); + _storage.Clear(); + + _veryLongArrayLength = instArrayLength; + } + else + { + // If there are already instances loaded into the _storage, train on them. + return false; + } + } + // Special case that happens only when _storage is empty + if (_storage.Count == 0) + { + if (!CheckAndAllocate()) + return false; + _indexInCurArray = 0; + } + // Check if instArray can be fitted in the current setup. + else if (!FitsInCurArray(instArrayLength)) + { + // Check if we reached the end of _storage. If so try to allocate a new very long array. + // Otherwise, there are more very long arrays left, just move to the next one. + if (_storageIndex == _storage.Count - 1) + { + if (!CheckAndAllocate()) + return false; + } + _indexInCurArray = 0; + _storageIndex++; + } + Array.Copy(instArray, 0, _storage[_storageIndex].Buffer, _indexInCurArray, instArrayLength); + _indexInCurArray += instArrayLength; + return true; + } + + /// + /// This is a soft clear, meaning that it doesn't reallocate, only sets _storageIndex and + /// _indexInCurArray to 0. + /// + public void ResetIndexing() + { + _storageIndex = 0; + _indexInCurArray = 0; + } + + /// + /// Gives an array of . + /// + /// The size of array to give + /// + /// + public void GiveArrayOfSize(int size, out GCHandle? outGcHandle, out int outArrayStartIndex) + { + // Generally it is the user responsibility to not ask for an array of a size that has not been + // previously allocated. + + // In case no allocation has occured. + if (_storage.Count == 0) + { + outGcHandle = null; + outArrayStartIndex = 0; + } + else + { + // Check if the array fits in _storage[_storageIndex]. + if (!FitsInCurArray(size)) + { + // If not, it must be in the next very long array. + _storageIndex++; + _indexInCurArray = 0; + } + outGcHandle = _storage[_storageIndex].GcHandle; + outArrayStartIndex = _indexInCurArray; + _indexInCurArray += size; + } + } + + private void DeallocateVeryLongArrays() + { + foreach (var veryLongArray in _storage) + veryLongArray.Free(); + } + + public void Dispose() + { + DeallocateVeryLongArrays(); + } + } + + /// + /// This class manages the buffering for instances + /// + private sealed class InputDataManager : IDisposable + { + // This ArrayManager is used for indices of instances + private readonly ArrayManager _instIndices; + // This ArrayManager is used for values of instances + private readonly ArrayManager _instValues; + // This is a list of the properties of instances that are buffered. + private readonly List _instanceProperties; + private readonly FloatLabelCursor.Factory _cursorFactory; + private FloatLabelCursor _cursor; + // This is used as a mechanism to make sure that the memorySize restriction is not violated. + private bool _cursorMoveNext; + // This is the index to go over the instances in instanceProperties + private int _instanceIndex; + // This is used to access AccelMemBudget, AccelChunkSize and UsedMemory + private readonly SymSgdClassificationTrainer _trainer; + private readonly IChannel _ch; + + // Whether memorySize was big enough to load the entire instances into the buffer + private bool _isFullyLoaded; + public bool IsFullyLoaded => _isFullyLoaded; + public int Count => _instanceProperties.Count; + + // Tells if we have gone through the dataset entirely. + public bool FinishedTheLoad => !_cursorMoveNext; + + public InputDataManager(SymSgdClassificationTrainer trainer, FloatLabelCursor.Factory cursorFactory, IChannel ch) + { + _instIndices = new ArrayManager(trainer, ch); + _instValues = new ArrayManager(trainer, ch); + _instanceProperties = new List(); + + _cursorFactory = cursorFactory; + _ch = ch; + _cursor = cursorFactory.Create(); + _cursorMoveNext = _cursor.MoveNext(); + _isFullyLoaded = true; + + _instanceIndex = 0; + + _trainer = trainer; + } + + // Has to be called for cursoring through the data + public void RestartLoading(bool needShuffle, IHost host) + { + _cursor.Dispose(); + if (needShuffle) + _cursor = _cursorFactory.Create(RandomUtils.Create(host.Rand.Next())); + else + _cursor = _cursorFactory.Create(); + _cursorMoveNext = _cursor.MoveNext(); + } + + /// + /// This method tries to load as much as possible from the cursor into the buffer until the memorySize is reached. + /// + public void LoadAsMuchAsPossible() + { + _instValues.ResetIndexing(); + _instIndices.ResetIndexing(); + _instanceProperties.Clear(); + + while (_cursorMoveNext) + { + int featureCount = _cursor.Features.Count; + // If the instance has no feature, ignore it! + if (featureCount == 0) + { + _cursorMoveNext = _cursor.MoveNext(); + continue; + } + + // We assume that cursor.Features.values are represented by Float and cursor.Features.indices are represented by int + // We conservatively assume that an instance is sparse and therefore, it has an array of Floats and ints for values and indices + int perNonZeroInBytes = sizeof(Float) + sizeof(int); + if (featureCount > _trainer.AcceleratedMemoryBudgetBytes / perNonZeroInBytes) + { + // Hopefully this never happens. But the memorySize must >= perNonZeroInBytes * length(the longest instance). + throw _ch.Except("Acceleration memory budget is too small! Need at least {0} MB for at least one of the instances", + featureCount * perNonZeroInBytes / (1024 * 1024)); + } + + bool couldLoad = true; + if (!_cursor.Features.IsDense) + // If it is a sparse instance, load its indices to instIndices buffer + couldLoad = _instIndices.AddToStorage(_cursor.Features.Indices, featureCount); + // Load values of an instance into instValues + if (couldLoad) + couldLoad = _instValues.AddToStorage(_cursor.Features.Values, featureCount); + + // If the load was successful, load the instance properties to instanceProperties + if (couldLoad) + { + float label = _cursor.Label; + InstanceProperties prop = new InstanceProperties(featureCount, label, _cursor.Features.IsDense); + _instanceProperties.Add(prop); + + _cursorMoveNext = _cursor.MoveNext(); + + if (_instanceProperties.Count > (1 << 30)) + { + // If it happened to be the case that we have so much memory that we were able to load (1<<30) instances, + // break. This is because in such a case _instanceProperties can only be addressed by int32 and (1<<30) is + // getting close to the limits. This should rarely happen! + _isFullyLoaded = false; + break; + } + } + else + { + // If couldLoad fails at any point (which is becuase of memorySize), isFullyLoaded becomes false forever + _isFullyLoaded = false; + break; + } + } + } + + public void PrepareCursoring() + { + _instanceIndex = 0; + _instIndices.ResetIndexing(); + _instValues.ResetIndexing(); + } + + /// + /// This method provides instances stored in the buffer in a sequential order. Note that method PrepareCursoring should be called before using this method. + /// + /// The property of the given instance. It is set to null in case there are no more instance. + /// + /// The offset for the indices array. + /// + /// The offset for the values array. + /// Retruns whether output is valid. Otherwise we have gone through the entire loaded instances. + public bool GiveNextInstance(out InstanceProperties? prop, out GCHandle? indicesGcHandle, out int indicesStartIndex, + out GCHandle? valuesGcHandle, out int valuesStartIndex) + { + if (_instanceIndex == _instanceProperties.Count) + { + // We hit the end. + prop = null; + indicesGcHandle = null; + indicesStartIndex = 0; + valuesGcHandle = null; + valuesStartIndex = 0; + return false; + } + prop = _instanceProperties[_instanceIndex]; + if (!prop.Value.IsDense) + { + // If sparse, set indices array accordingly. + _instIndices.GiveArrayOfSize(prop.Value.FeatureCount, out indicesGcHandle, out indicesStartIndex); + } + else + { + indicesGcHandle = null; + indicesStartIndex = 0; + } + // Load values here. + _instValues.GiveArrayOfSize(prop.Value.FeatureCount, out valuesGcHandle, out valuesStartIndex); + _instanceIndex++; + return true; + } + + public void Dispose() + { + _cursor.Dispose(); + _instIndices.Dispose(); + _instValues.Dispose(); + } + } + + private TPredictor TrainCore(IChannel ch, RoleMappedData data, LinearPredictor predictor, int weightSetCount) + { + int numFeatures = data.Schema.Feature.Type.VectorSize; + var cursorFactory = new FloatLabelCursor.Factory(data, CursOpt.Label | CursOpt.Features | CursOpt.Weight); + int numThreads = 1; + ch.CheckUserArg(numThreads > 0, nameof(_args.NumberOfThreads), + "The number of threads must be either null or a positive integer."); + + var positiveInstanceWeight = _args.PositiveInstanceWeight; + VBuffer weights = default; + float bias = 0.0f; + if (predictor != null) + { + predictor.GetFeatureWeights(ref weights); + VBufferUtils.Densify(ref weights); + bias = predictor.Bias; + } + else + weights = VBufferUtils.CreateDense(numFeatures); + + // Reference: Parasail. SymSGD. + bool tuneLR = _args.LearningRate == null; + var lr = _args.LearningRate ?? 1.0f; + + bool tuneNumLocIter = (_args.UpdateFrequency == null); + var numLocIter = _args.UpdateFrequency ?? 1; + + var l2Const = _args.L2Regularization; + var piw = _args.PositiveInstanceWeight; + + // This is state of the learner that is shared with the native code. + State state = new State(); + GCHandle stateGCHandle = default; + try + { + stateGCHandle = GCHandle.Alloc(state, GCHandleType.Pinned); + + state.TotalInstancesProcessed = 0; + using (InputDataManager inputDataManager = new InputDataManager(this, cursorFactory, ch)) + { + bool shouldInitialize = true; + using (var pch = Host.StartProgressChannel("Preprocessing")) + inputDataManager.LoadAsMuchAsPossible(); + + int iter = 0; + if (inputDataManager.IsFullyLoaded) + ch.Info("Data fully loaded into memory."); + using (var pch = Host.StartProgressChannel("Training")) + { + if (inputDataManager.IsFullyLoaded) + { + pch.SetHeader(new ProgressHeader(new[] { "iterations" }), + entry => entry.SetProgress(0, state.PassIteration, _args.NumberOfIterations)); + // If fully loaded, call the SymSGDNative and do not come back until learned for all iterations. + Native.LearnAll(inputDataManager, tuneLR, ref lr, l2Const, piw, weights.Values, ref bias, numFeatures, + _args.NumberOfIterations, numThreads, tuneNumLocIter, ref numLocIter, _args.Tolerance, _args.Shuffle, shouldInitialize, stateGCHandle); + shouldInitialize = false; + } + else + { + pch.SetHeader(new ProgressHeader(new[] { "iterations" }), + entry => entry.SetProgress(0, iter, _args.NumberOfIterations)); + + // Since we loaded data in batch sizes, multiple passes over the loaded data is feasible. + int numPassesForABatch = inputDataManager.Count / 10000; + while (iter < _args.NumberOfIterations) + { + // We want to train on the final passes thoroughly (without learning on the same batch multiple times) + // This is for fine tuning the AUC. Experimentally, we found that 1 or 2 passes is enough + int numFinalPassesToTrainThoroughly = 2; + // We also do not want to learn for more passes than what the user asked + int numPassesForThisBatch = Math.Min(numPassesForABatch, _args.NumberOfIterations - iter - numFinalPassesToTrainThoroughly); + // If all of this leaves us with 0 passes, then set numPassesForThisBatch to 1 + numPassesForThisBatch = Math.Max(1, numPassesForThisBatch); + state.PassIteration = iter; + Native.LearnAll(inputDataManager, tuneLR, ref lr, l2Const, piw, weights.Values, ref bias, numFeatures, + numPassesForThisBatch, numThreads, tuneNumLocIter, ref numLocIter, _args.Tolerance, _args.Shuffle, shouldInitialize, stateGCHandle); + shouldInitialize = false; + + // Check if we are done with going through the data + if (inputDataManager.FinishedTheLoad) + { + iter += numPassesForThisBatch; + // Check if more passes are left + if (iter < _args.NumberOfIterations) + inputDataManager.RestartLoading(_args.Shuffle, Host); + } + + // If more passes are left, load as much as possible + if (iter < _args.NumberOfIterations) + inputDataManager.LoadAsMuchAsPossible(); + } + } + + // Maps back the dense features that are mislocated + if (numThreads > 1) + Native.MapBackWeightVector(weights.Values, stateGCHandle); + Native.DeallocateSequentially(stateGCHandle); + } + } + } + finally + { + if (stateGCHandle.IsAllocated) + stateGCHandle.Free(); + } + return CreatePredictor(weights, bias); + } + + private void CheckLabel(RoleMappedData examples, out int weightSetCount) + { + examples.CheckBinaryLabel(); + weightSetCount = 1; + } + + private long AcceleratedMemoryBudgetBytes => _args.MemorySize * 1024 * 1024; + private long UsedMemory { get; set; } + + private static unsafe class Native + { + internal const string DllName = "SymSgdNative"; + + [DllImport(DllName), SuppressUnmanagedCodeSecurity] + private static extern void LearnAll(int totalNumInstances, int* instSizes, int** instIndices, + float** instValues, float* labels, bool tuneLR, ref float lr, float l2Const, float piw, float* weightVector, ref float bias, + int numFeatres, int numPasses, int numThreads, bool tuneNumLocIter, ref int numLocIter, float tolerance, bool needShuffle, bool shouldInitialize, State* state); + + /// + /// This method puts all of the buffered instances in array of pointers to pass it to SymSGDNative. + /// + /// The buffered data + /// Specifies if SymSGD should tune alpha automatically + /// Initial learning rate + /// + /// + /// The storage for the weight vector + /// bias + /// Number of features + /// Number of passes + /// Number of threads + /// Specifies if SymSGD should tune numLocIter automatically + /// Number of thread local iterations of SGD before combining with the global model + /// Tolerance for the amount of decrease in the total loss in consecutive passes + /// Specifies if data needs to be shuffled + /// Specifies if this is the first time to run SymSGD + /// + public static void LearnAll(InputDataManager inputDataManager, bool tuneLR, + ref float lr, float l2Const, float piw, float[] weightVector, ref float bias, int numFeatres, int numPasses, + int numThreads, bool tuneNumLocIter, ref int numLocIter, float tolerance, bool needShuffle, bool shouldInitialize, GCHandle stateGCHandle) + { + inputDataManager.PrepareCursoring(); + + int totalNumInstances = inputDataManager.Count; + // Each instance has a pointer to indices array and a pointer to values array + int*[] arrayIndicesPointers = new int*[totalNumInstances]; + float*[] arrayValuesPointers = new float*[totalNumInstances]; + // Labels of the instances + float[] instLabels = new float[totalNumInstances]; + // Sizes of each inst + int[] instSizes = new int[totalNumInstances]; + + int instanceIndex = 0; + // Going through the buffer to set the properties and the pointers + while (inputDataManager.GiveNextInstance(out InstanceProperties? prop, out GCHandle? indicesGcHandle, out int indicesStartIndex, out GCHandle? valuesGcHandle, out int valuesStartIndex)) + { + if (prop.Value.IsDense) + { + arrayIndicesPointers[instanceIndex] = null; + } + else + { + int* pIndicesArray = (int*)indicesGcHandle.Value.AddrOfPinnedObject(); + arrayIndicesPointers[instanceIndex] = &pIndicesArray[indicesStartIndex]; + } + float* pValuesArray = (float*)valuesGcHandle.Value.AddrOfPinnedObject(); + arrayValuesPointers[instanceIndex] = &pValuesArray[valuesStartIndex]; + + instLabels[instanceIndex] = prop.Value.Label; + instSizes[instanceIndex] = prop.Value.FeatureCount; + instanceIndex++; + } + + fixed (float* pweightVector = &weightVector[0]) + fixed (int** pIndicesPointer = &arrayIndicesPointers[0]) + fixed (float** pValuesPointer = &arrayValuesPointers[0]) + fixed (int* pInstSizes = &instSizes[0]) + fixed (float* pInstLabels = &instLabels[0]) + { + LearnAll(totalNumInstances, pInstSizes, pIndicesPointer, pValuesPointer, pInstLabels, tuneLR, ref lr, l2Const, piw, + pweightVector, ref bias, numFeatres, numPasses, numThreads, tuneNumLocIter, ref numLocIter, tolerance, needShuffle, shouldInitialize, (State*)stateGCHandle.AddrOfPinnedObject()); + } + } + + [DllImport(DllName), SuppressUnmanagedCodeSecurity] + private static extern void MapBackWeightVector(float* weightVector, State* state); + + /// + /// Maps back the dense feature to the correct position + /// + /// The weight vector + /// + public static void MapBackWeightVector(float[] weightVector, GCHandle stateGCHandle) + { + fixed (float* pweightVector = &weightVector[0]) + MapBackWeightVector(pweightVector, (State*)stateGCHandle.AddrOfPinnedObject()); + } + + [DllImport(DllName), SuppressUnmanagedCodeSecurity] + private static extern void DeallocateSequentially(State* state); + + public static void DeallocateSequentially(GCHandle stateGCHandle) + { + DeallocateSequentially((State*)stateGCHandle.AddrOfPinnedObject()); + } + } + + /// + /// This is the state of a SymSGD learner that is shared between the managed and native code. + /// + [StructLayout(LayoutKind.Explicit)] + internal unsafe struct State + { +#pragma warning disable 649 // never assigned + [FieldOffset(0x00)] + public readonly int NumLearners; + [FieldOffset(0x04)] + public int TotalInstancesProcessed; + [FieldOffset(0x08)] + public readonly void* Learners; + [FieldOffset(0x10)] + public readonly void* FreqFeatUnorderedMap; + [FieldOffset(0x18)] + public readonly int* FreqFeatDirectMap; + [FieldOffset(0x20)] + public readonly int NumFrequentFeatures; + [FieldOffset(0x24)] + public int PassIteration; + [FieldOffset(0x28)] + public readonly float WeightScaling; +#pragma warning restore 649 // never assigned + } + } + +} diff --git a/src/Microsoft.ML.HalLearners/doc.xml b/src/Microsoft.ML.HalLearners/doc.xml new file mode 100644 index 0000000000..15022b4aa2 --- /dev/null +++ b/src/Microsoft.ML.HalLearners/doc.xml @@ -0,0 +1,45 @@ + + + + + + + Train an OLS regression model. + + + Ordinary least squares (OLS) is a parameterized regression method. + It assumes that the conditional mean of the dependent variable follows a linear function of the dependent variables. + The parameters of the regressor can be estimated by minimizing the squares of the difference between observed values and the predictions. + + + + new OrdinaryLeastSquaresRegressor + { + L2Weight = 0.1, + PerParameterSignificance = false, + NormalizeFeatures = Microsoft.ML.Models.NormalizeOption.Yes + } + + + + + + Parallel Stochastic Gradient Descent trainer. + + + Stochastic gradient descent (SGD) is an interative algorithm + that optimizes a differentiable objective function. SYMSGD parallelizes SGD using Sound Combiners. + + + + new SymSgdBinaryClassifier() + { + NumberOfIterations = 50, + L2Regularization = 0, + Shuffle = true + } + + + + + \ No newline at end of file diff --git a/src/Microsoft.ML.ImageAnalytics/EntryPoints/ImageAnalytics.cs b/src/Microsoft.ML.ImageAnalytics/EntryPoints/ImageAnalytics.cs new file mode 100644 index 0000000000..97c613485f --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/EntryPoints/ImageAnalytics.cs @@ -0,0 +1,79 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.ImageAnalytics.EntryPoints; + +[assembly: LoadableClass(typeof(void), typeof(ImageAnalytics), null, typeof(SignatureEntryPointModule), "ImageAnalytics")] +namespace Microsoft.ML.Runtime.ImageAnalytics.EntryPoints +{ + public static class ImageAnalytics + { + [TlcModule.EntryPoint(Name = "Transforms.ImageLoader", Desc = ImageLoaderTransform.Summary, + UserName = ImageLoaderTransform.UserName, ShortName = ImageLoaderTransform.LoaderSignature)] + public static CommonOutputs.TransformOutput ImageLoader(IHostEnvironment env, ImageLoaderTransform.Arguments input) + { + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImageLoaderTransform", input); + var xf = new ImageLoaderTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, xf, input.Data), + OutputData = xf + }; + } + + [TlcModule.EntryPoint(Name = "Transforms.ImageResizer", Desc = ImageResizerTransform.Summary, + UserName = ImageResizerTransform.UserName, ShortName = ImageResizerTransform.LoaderSignature)] + public static CommonOutputs.TransformOutput ImageResizer(IHostEnvironment env, ImageResizerTransform.Arguments input) + { + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImageResizerTransform", input); + var xf = new ImageResizerTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, xf, input.Data), + OutputData = xf + }; + } + + [TlcModule.EntryPoint(Name = "Transforms.ImagePixelExtractor", Desc = ImagePixelExtractorTransform.Summary, + UserName = ImagePixelExtractorTransform.UserName, ShortName = ImagePixelExtractorTransform.LoaderSignature)] + public static CommonOutputs.TransformOutput ImagePixelExtractor(IHostEnvironment env, ImagePixelExtractorTransform.Arguments input) + { + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImagePixelExtractorTransform", input); + var xf = new ImagePixelExtractorTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, xf, input.Data), + OutputData = xf + }; + } + + [TlcModule.EntryPoint(Name = "Transforms.ImageGrayscale", Desc = ImageGrayscaleTransform.Summary, + UserName = ImageGrayscaleTransform.UserName, ShortName = ImageGrayscaleTransform.LoaderSignature)] + public static CommonOutputs.TransformOutput ImageGrayscale(IHostEnvironment env, ImageGrayscaleTransform.Arguments input) + { + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "ImageGrayscaleTransform", input); + var xf = new ImageGrayscaleTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, xf, input.Data), + OutputData = xf + }; + } + + [TlcModule.EntryPoint(Name = "Transforms.VectorToImage", Desc = VectorToImageTransform.Summary, + UserName = VectorToImageTransform.UserName, ShortName = VectorToImageTransform.LoaderSignature)] + public static CommonOutputs.TransformOutput VectorToImage(IHostEnvironment env, VectorToImageTransform.Arguments input) + { + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "VectorToImageTransform", input); + var xf = new VectorToImageTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, xf, input.Data), + OutputData = xf + }; + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs new file mode 100644 index 0000000000..7a267cf1b8 --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/ImageGrayscaleTransform.cs @@ -0,0 +1,171 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using System.Drawing.Imaging; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.ImageAnalytics; + +[assembly: LoadableClass(ImageGrayscaleTransform.Summary, typeof(ImageGrayscaleTransform), typeof(ImageGrayscaleTransform.Arguments), typeof(SignatureDataTransform), + ImageGrayscaleTransform.UserName, "ImageGrayscaleTransform", "ImageGrayscale")] + +[assembly: LoadableClass(ImageGrayscaleTransform.Summary, typeof(ImageGrayscaleTransform), null, typeof(SignatureLoadDataTransform), + ImageGrayscaleTransform.UserName, ImageGrayscaleTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + // REVIEW: Rewrite as LambdaTransform to simplify. + // REVIEW: Should it be separate transform or part of ImageResizerTransform? + /// + /// Transform which takes one or many columns of type in IDataView and + /// convert them to greyscale representation of the same image. + /// + public sealed class ImageGrayscaleTransform : OneToOneTransformBase + { + public sealed class Column : OneToOneColumn + { + public static Column Parse(string str) + { + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + return TryUnparseCore(sb); + } + } + + public class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 1)] + public Column[] Column; + } + + internal const string Summary = "Convert image into grayscale."; + + internal const string UserName = "Image Greyscale Transform"; + public const string LoaderSignature = "ImageGrayscaleTransform"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "IMGGRAYT", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + private const string RegistrationName = "ImageGrayscale"; + + // Public constructor corresponding to SignatureDataTransform. + public ImageGrayscaleTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, env.CheckRef(args, nameof(args)).Column, input, t => t is ImageType ? null : "Expected Image type") + { + Host.AssertNonEmpty(Infos); + Host.Assert(Infos.Length == Utils.Size(args.Column)); + Metadata.Seal(); + } + + private ImageGrayscaleTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, t => t is ImageType ? null : "Expected Image type") + { + Host.AssertValue(ctx); + // *** Binary format *** + // + Host.AssertNonEmpty(Infos); + Metadata.Seal(); + } + + public static ImageGrayscaleTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + ctx.CheckAtModel(GetVersionInfo()); + return h.Apply("Loading Model", ch => new ImageGrayscaleTransform(h, ctx, input)); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // + SaveBase(ctx); + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Assert(0 <= iinfo & iinfo < Infos.Length); + return Infos[iinfo].TypeSrc; + } + + private static readonly ColorMatrix _grayscaleColorMatrix = new ColorMatrix( + new float[][] + { + new float[] {.3f, .3f, .3f, 0, 0}, + new float[] {.59f, .59f, .59f, 0, 0}, + new float[] {.11f, .11f, .11f, 0, 0}, + new float[] {0, 0, 0, 1, 0}, + new float[] {0, 0, 0, 0, 1} + }); + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValueOrNull(ch); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + + var src = default(Bitmap); + var getSrc = GetSrcGetter(input, iinfo); + + disposer = + () => + { + if (src != null) + { + src.Dispose(); + src = null; + } + }; + + ValueGetter del = + (ref Bitmap dst) => + { + if (dst != null) + dst.Dispose(); + + getSrc(ref src); + if (src == null || src.Height <= 0 || src.Width <= 0) + return; + + dst = new Bitmap(src.Width, src.Height); + ImageAttributes attributes = new ImageAttributes(); + attributes.SetColorMatrix(_grayscaleColorMatrix); + var srcRectangle = new Rectangle(0, 0, src.Width, src.Height); + using (var g = Graphics.FromImage(dst)) + { + g.DrawImage(src, srcRectangle, 0, 0, src.Width, src.Height, GraphicsUnit.Pixel, attributes); + } + Host.Assert(dst.Width == src.Width && dst.Height == src.Height); + }; + + return del; + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs new file mode 100644 index 0000000000..488c710743 --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/ImageLoaderTransform.cs @@ -0,0 +1,178 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using System.IO; +using System.Text; +using Microsoft.ML.Runtime.ImageAnalytics; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(ImageLoaderTransform.Summary, typeof(ImageLoaderTransform), typeof(ImageLoaderTransform.Arguments), typeof(SignatureDataTransform), + ImageLoaderTransform.UserName, "ImageLoaderTransform", "ImageLoader")] + +[assembly: LoadableClass(ImageLoaderTransform.Summary, typeof(ImageLoaderTransform), null, typeof(SignatureLoadDataTransform), + ImageLoaderTransform.UserName, ImageLoaderTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + // REVIEW: Rewrite as LambdaTransform to simplify. + /// + /// Transform which takes one or many columns of type and loads them as + /// + public sealed class ImageLoaderTransform : OneToOneTransformBase + { + public sealed class Column : OneToOneColumn + { + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + return TryUnparseCore(sb); + } + } + + public sealed class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", + ShortName = "col", SortOrder = 1)] + public Column[] Column; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Folder where to search for images", ShortName = "folder")] + public string ImageFolder; + } + + internal const string Summary = "Load images from files."; + internal const string UserName = "Image Loader Transform"; + public const string LoaderSignature = "ImageLoaderTransform"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "IMGLOADT", + //verWrittenCur: 0x00010001, // Initial + verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap + verReadableCur: 0x00010002, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private readonly ImageType _type; + private readonly string _imageFolder; + + private const string RegistrationName = "ImageLoader"; + + // Public constructor corresponding to SignatureDataTransform. + public ImageLoaderTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, env.CheckRef(args, nameof(args)).Column, input, TestIsText) + { + Host.AssertNonEmpty(Infos); + _imageFolder = args.ImageFolder; + Host.Assert(Infos.Length == Utils.Size(args.Column)); + _type = new ImageType(); + Metadata.Seal(); + } + + private ImageLoaderTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, TestIsText) + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + _imageFolder = ctx.Reader.ReadString(); + _type = new ImageType(); + Metadata.Seal(); + } + + public static ImageLoaderTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + ctx.CheckAtModel(GetVersionInfo()); + return h.Apply("Loading Model", ch => new ImageLoaderTransform(h, ctx, input)); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // + ctx.Writer.Write(_imageFolder); + SaveBase(ctx); + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Check(0 <= iinfo && iinfo < Infos.Length); + return _type; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValue(ch, nameof(ch)); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + disposer = null; + + var getSrc = GetSrcGetter(input, iinfo); + DvText src = default; + ValueGetter del = + (ref Bitmap dst) => + { + if (dst != null) + { + dst.Dispose(); + dst = null; + } + + getSrc(ref src); + + if (src.Length > 0) + { + // Catch exceptions and pass null through. Should also log failures... + try + { + string path = src.ToString(); + if (!string.IsNullOrWhiteSpace(_imageFolder)) + path = Path.Combine(_imageFolder, path); + dst = new Bitmap(path); + } + catch (Exception e) + { + // REVIEW: We catch everything since the documentation for new Bitmap(string) + // appears to be incorrect. When the file isn't found, it throws an ArgumentException, + // while the documentation says FileNotFoundException. Not sure what it will throw + // in other cases, like corrupted file, etc. + + // REVIEW : Log failures. + ch.Info(e.Message); + ch.Info(e.StackTrace); + dst = null; + } + } + }; + return del; + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs new file mode 100644 index 0000000000..de0aa98124 --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/ImagePixelExtractorTransform.cs @@ -0,0 +1,541 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.ImageAnalytics; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(ImagePixelExtractorTransform.Summary, typeof(ImagePixelExtractorTransform), typeof(ImagePixelExtractorTransform.Arguments), typeof(SignatureDataTransform), + ImagePixelExtractorTransform.UserName, "ImagePixelExtractorTransform", "ImagePixelExtractor")] + +[assembly: LoadableClass(ImagePixelExtractorTransform.Summary, typeof(ImagePixelExtractorTransform), null, typeof(SignatureLoadDataTransform), + ImagePixelExtractorTransform.UserName, ImagePixelExtractorTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + // REVIEW: Rewrite as LambdaTransform to simplify. + /// + /// Transform which takes one or many columns of and convert them into vector representation. + /// + public sealed class ImagePixelExtractorTransform : OneToOneTransformBase + { + public class Column : OneToOneColumn + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] + public bool? UseAlpha; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] + public bool? UseRed; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] + public bool? UseGreen; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] + public bool? UseBlue; + + // REVIEW: Consider turning this into an enum that allows for pixel, line, or planar interleaving. + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in ARGB order", ShortName = "interleave")] + public bool? InterleaveArgb; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to convert to floating point", ShortName = "conv")] + public bool? Convert; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] + public Single? Offset; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] + public Single? Scale; + + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + if (UseAlpha != null || UseRed != null || UseGreen != null || UseBlue != null || Convert != null || + Offset != null || Scale != null || InterleaveArgb != null) + { + return false; + } + return TryUnparseCore(sb); + } + } + + public class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 1)] + public Column[] Column; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] + public bool UseAlpha = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] + public bool UseRed = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] + public bool UseGreen = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] + public bool UseBlue = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in ARGB order", ShortName = "interleave")] + public bool InterleaveArgb = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to convert to floating point", ShortName = "conv")] + public bool Convert = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] + public Single? Offset; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] + public Single? Scale; + } + + /// + /// Which color channels are extracted. Note that these values are serialized so should not be modified. + /// + [Flags] + private enum ColorBits : byte + { + Alpha = 0x01, + Red = 0x02, + Green = 0x04, + Blue = 0x08, + + All = Alpha | Red | Green | Blue + } + + private sealed class ColInfoEx + { + public readonly ColorBits Colors; + public readonly byte Planes; + + public readonly bool Convert; + public readonly Single Offset; + public readonly Single Scale; + public readonly bool Interleave; + + public bool Alpha { get { return (Colors & ColorBits.Alpha) != 0; } } + public bool Red { get { return (Colors & ColorBits.Red) != 0; } } + public bool Green { get { return (Colors & ColorBits.Green) != 0; } } + public bool Blue { get { return (Colors & ColorBits.Blue) != 0; } } + + public ColInfoEx(Column item, Arguments args) + { + if (item.UseAlpha ?? args.UseAlpha) { Colors |= ColorBits.Alpha; Planes++; } + if (item.UseRed ?? args.UseRed) { Colors |= ColorBits.Red; Planes++; } + if (item.UseGreen ?? args.UseGreen) { Colors |= ColorBits.Green; Planes++; } + if (item.UseBlue ?? args.UseBlue) { Colors |= ColorBits.Blue; Planes++; } + Contracts.CheckUserArg(Planes > 0, nameof(item.UseRed), "Need to use at least one color plane"); + + Interleave = item.InterleaveArgb ?? args.InterleaveArgb; + + Convert = item.Convert ?? args.Convert; + if (!Convert) + { + Offset = 0; + Scale = 1; + } + else + { + Offset = item.Offset ?? args.Offset ?? 0; + Scale = item.Scale ?? args.Scale ?? 1; + Contracts.CheckUserArg(FloatUtils.IsFinite(Offset), nameof(item.Offset)); + Contracts.CheckUserArg(FloatUtils.IsFiniteNonZero(Scale), nameof(item.Scale)); + } + } + + public ColInfoEx(ModelLoadContext ctx) + { + Contracts.AssertValue(ctx); + + // *** Binary format *** + // byte: colors + // byte: convert + // Float: offset + // Float: scale + // byte: separateChannels + Colors = (ColorBits)ctx.Reader.ReadByte(); + Contracts.CheckDecode(Colors != 0); + Contracts.CheckDecode((Colors & ColorBits.All) == Colors); + + // Count the planes. + int planes = (int)Colors; + planes = (planes & 0x05) + ((planes >> 1) & 0x05); + planes = (planes & 0x03) + ((planes >> 2) & 0x03); + Planes = (byte)planes; + Contracts.Assert(0 < Planes & Planes <= 4); + + Convert = ctx.Reader.ReadBoolByte(); + Offset = ctx.Reader.ReadFloat(); + Contracts.CheckDecode(FloatUtils.IsFinite(Offset)); + Scale = ctx.Reader.ReadFloat(); + Contracts.CheckDecode(FloatUtils.IsFiniteNonZero(Scale)); + Contracts.CheckDecode(Convert || Offset == 0 && Scale == 1); + Interleave = ctx.Reader.ReadBoolByte(); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.AssertValue(ctx); + +#if DEBUG + // This code is used in deserialization - assert that it matches what we computed above. + int planes = (int)Colors; + planes = (planes & 0x05) + ((planes >> 1) & 0x05); + planes = (planes & 0x03) + ((planes >> 2) & 0x03); + Contracts.Assert(planes == Planes); +#endif + + // *** Binary format *** + // byte: colors + // byte: convert + // Float: offset + // Float: scale + // byte: separateChannels + Contracts.Assert(Colors != 0); + Contracts.Assert((Colors & ColorBits.All) == Colors); + ctx.Writer.Write((byte)Colors); + ctx.Writer.WriteBoolByte(Convert); + Contracts.Assert(FloatUtils.IsFinite(Offset)); + ctx.Writer.Write(Offset); + Contracts.Assert(FloatUtils.IsFiniteNonZero(Scale)); + Contracts.Assert(Convert || Offset == 0 && Scale == 1); + ctx.Writer.Write(Scale); + ctx.Writer.WriteBoolByte(Interleave); + } + } + + internal const string Summary = "Extract color plane(s) from an image. Options include scaling, offset and conversion to floating point."; + internal const string UserName = "Image Pixel Extractor Transform"; + public const string LoaderSignature = "ImagePixelExtractor"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "IMGPXEXT", + //verWrittenCur: 0x00010001, // Initial + verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap + verReadableCur: 0x00010002, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private const string RegistrationName = "ImagePixelExtractor"; + + private readonly ColInfoEx[] _exes; + private readonly VectorType[] _types; + + // Public constructor corresponding to SignatureDataTransform. + public ImagePixelExtractorTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, Contracts.CheckRef(args, nameof(args)).Column, input, + t => t is ImageType ? null : "Expected Image type") + { + Host.AssertNonEmpty(Infos); + Host.Assert(Infos.Length == Utils.Size(args.Column)); + + _exes = new ColInfoEx[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + { + var item = args.Column[i]; + _exes[i] = new ColInfoEx(item, args); + } + + _types = ConstructTypes(true); + } + + private ImagePixelExtractorTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, t => t is ImageType ? null : "Expected Image type") + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + // + // foreach added column + // ColInfoEx + Host.AssertNonEmpty(Infos); + _exes = new ColInfoEx[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + _exes[i] = new ColInfoEx(ctx); + + _types = ConstructTypes(false); + } + + public static ImagePixelExtractorTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + ctx.CheckAtModel(GetVersionInfo()); + + return h.Apply("Loading Model", + ch => + { + // *** Binary format *** + // int: sizeof(Float) + // + int cbFloat = ctx.Reader.ReadInt32(); + ch.CheckDecode(cbFloat == sizeof(Single)); + return new ImagePixelExtractorTransform(h, ctx, input); + }); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Float) + // + // foreach added column + // ColInfoEx + ctx.Writer.Write(sizeof(Single)); + SaveBase(ctx); + + Host.Assert(_exes.Length == Infos.Length); + for (int i = 0; i < _exes.Length; i++) + _exes[i].Save(ctx); + } + + private VectorType[] ConstructTypes(bool user) + { + var types = new VectorType[Infos.Length]; + for (int i = 0; i < Infos.Length; i++) + { + var info = Infos[i]; + var ex = _exes[i]; + Host.Assert(ex.Planes > 0); + + var type = Source.Schema.GetColumnType(info.Source) as ImageType; + Host.Assert(type != null); + if (type.Height <= 0 || type.Width <= 0) + { + // REVIEW: Could support this case by making the destination column be variable sized. + // However, there's no mechanism to communicate the dimensions through with the pixel data. + string name = Source.Schema.GetColumnName(info.Source); + throw user ? + Host.ExceptUserArg(nameof(Arguments.Column), "Column '{0}' does not have known size", name) : + Host.Except("Column '{0}' does not have known size", name); + } + int height = type.Height; + int width = type.Width; + Host.Assert(height > 0); + Host.Assert(width > 0); + Host.Assert((long)height * width <= int.MaxValue / 4); + + if (ex.Interleave) + types[i] = new VectorType(ex.Convert ? NumberType.Float : NumberType.U1, height, width, ex.Planes); + else + types[i] = new VectorType(ex.Convert ? NumberType.Float : NumberType.U1, ex.Planes, height, width); + } + Metadata.Seal(); + return types; + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Assert(0 <= iinfo & iinfo < Infos.Length); + return _types[iinfo]; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValueOrNull(ch); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + + if (_exes[iinfo].Convert) + return GetGetterCore(input, iinfo, out disposer); + return GetGetterCore(input, iinfo, out disposer); + } + + //REVIEW Rewrite it to where TValue : IConvertible + private ValueGetter> GetGetterCore(IRow input, int iinfo, out Action disposer) + { + var type = _types[iinfo]; + Host.Assert(type.DimCount == 3); + + var ex = _exes[iinfo]; + + int planes = ex.Interleave ? type.GetDim(2) : type.GetDim(0); + int height = ex.Interleave ? type.GetDim(0) : type.GetDim(1); + int width = ex.Interleave ? type.GetDim(1) : type.GetDim(2); + + int size = type.ValueCount; + Host.Assert(size > 0); + Host.Assert(size == planes * height * width); + int cpix = height * width; + + var getSrc = GetSrcGetter(input, iinfo); + var src = default(Bitmap); + + disposer = + () => + { + if (src != null) + { + src.Dispose(); + src = null; + } + }; + + return + (ref VBuffer dst) => + { + getSrc(ref src); + Contracts.AssertValueOrNull(src); + + if (src == null) + { + dst = new VBuffer(size, 0, dst.Values, dst.Indices); + return; + } + + Host.Check(src.PixelFormat == System.Drawing.Imaging.PixelFormat.Format32bppArgb); + Host.Check(src.Height == height && src.Width == width); + + var values = dst.Values; + if (Utils.Size(values) < size) + values = new TValue[size]; + + Single offset = ex.Offset; + Single scale = ex.Scale; + Host.Assert(scale != 0); + + var vf = values as Single[]; + var vb = values as byte[]; + Host.Assert(vf != null || vb != null); + bool needScale = offset != 0 || scale != 1; + Host.Assert(!needScale || vf != null); + + bool a = ex.Alpha; + bool r = ex.Red; + bool g = ex.Green; + bool b = ex.Blue; + + int h = height; + int w = width; + + if (ex.Interleave) + { + int idst = 0; + for (int y = 0; y < h; ++y) + for (int x = 0; x < w; x++) + { + var pb = src.GetPixel(y, x); + if (vb != null) + { + if (a) { vb[idst++] = (byte)0; } + if (r) { vb[idst++] = pb.R; } + if (g) { vb[idst++] = pb.G; } + if (b) { vb[idst++] = pb.B; } + } + else if (!needScale) + { + if (a) { vf[idst++] = 0.0f; } + if (r) { vf[idst++] = pb.R; } + if (g) { vf[idst++] = pb.G; } + if (b) { vf[idst++] = pb.B; } + } + else + { + if (a) { vf[idst++] = 0.0f; } + if (r) { vf[idst++] = (pb.R - offset) * scale; } + if (g) { vf[idst++] = (pb.B - offset) * scale; } + if (b) { vf[idst++] = (pb.G - offset) * scale; } + } + } + Host.Assert(idst == size); + } + else + { + int idstMin = 0; + if (ex.Alpha) + { + // The image only has rgb but we need to supply alpha as well, so fake it up, + // assuming that it is 0xFF. + if (vf != null) + { + Single v = (0xFF - offset) * scale; + for (int i = 0; i < cpix; i++) + vf[i] = v; + } + else + { + for (int i = 0; i < cpix; i++) + vb[i] = 0xFF; + } + idstMin = cpix; + + // We've preprocessed alpha, avoid it in the + // scan operation below. + a = false; + } + + for (int y = 0; y < h; ++y) + { + int idstBase = idstMin + y * w; + + // Note that the bytes are in order BGR[A]. We arrange the layers in order ARGB. + if (vb != null) + { + for (int x = 0; x < w; x++, idstBase++) + { + var pb = src.GetPixel(x, y); + int idst = idstBase; + if (a) { vb[idst] = pb.A; idst += cpix; } + if (r) { vb[idst] = pb.R; idst += cpix; } + if (g) { vb[idst] = pb.G; idst += cpix; } + if (b) { vb[idst] = pb.B; idst += cpix; } + } + } + else if (!needScale) + { + for (int x = 0; x < w; x++, idstBase++) + { + var pb = src.GetPixel(x, y); + int idst = idstBase; + if (a) { vf[idst] = pb.A; idst += cpix; } + if (r) { vf[idst] = pb.R; idst += cpix; } + if (g) { vf[idst] = pb.G; idst += cpix; } + if (b) { vf[idst] = pb.B; idst += cpix; } + } + } + else + { + for (int x = 0; x < w; x++, idstBase++) + { + var pb = src.GetPixel(x, y); + int idst = idstBase; + if (a) { vf[idst] = (pb.A - offset) * scale; idst += cpix; } + if (r) { vf[idst] = (pb.R - offset) * scale; idst += cpix; } + if (g) { vf[idst] = (pb.G - offset) * scale; idst += cpix; } + if (b) { vf[idst] = (pb.B - offset) * scale; idst += cpix; } + } + } + } + } + + dst = new VBuffer(size, values, dst.Indices); + }; + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs b/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs new file mode 100644 index 0000000000..dd1abc9181 --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/ImageResizerTransform.cs @@ -0,0 +1,370 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.ImageAnalytics; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(ImageResizerTransform.Summary, typeof(ImageResizerTransform), typeof(ImageResizerTransform.Arguments), + typeof(SignatureDataTransform), ImageResizerTransform.UserName, "ImageResizerTransform", "ImageResizer")] + +[assembly: LoadableClass(ImageResizerTransform.Summary, typeof(ImageResizerTransform), null, typeof(SignatureLoadDataTransform), + ImageResizerTransform.UserName, ImageResizerTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + // REVIEW: Rewrite as LambdaTransform to simplify. + /// + /// Transform which takes one or many columns of and resize them to provided height and width. + /// + public sealed class ImageResizerTransform : OneToOneTransformBase + { + public enum ResizingKind : byte + { + [TGUI(Label = "Isotropic with Padding")] + IsoPad = 0, + + [TGUI(Label = "Isotropic with Cropping")] + IsoCrop = 1 + } + + public enum Anchor : byte + { + Right = 0, + Left = 1, + Top = 2, + Bottom = 3, + Center = 4 + } + + public sealed class Column : OneToOneColumn + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Width of the resized image", ShortName = "width")] + public int? ImageWidth; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Height of the resized image", ShortName = "height")] + public int? ImageHeight; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Resizing method", ShortName = "scale")] + public ResizingKind? Resizing; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Anchor for cropping", ShortName = "anchor")] + public Anchor? CropAnchor; + + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + if (ImageWidth != null || ImageHeight != null || Resizing != null || CropAnchor != null) + return false; + return TryUnparseCore(sb); + } + } + + public class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 1)] + public Column[] Column; + + [Argument(ArgumentType.Required, HelpText = "Resized width of the image", ShortName = "width")] + public int ImageWidth; + + [Argument(ArgumentType.Required, HelpText = "Resized height of the image", ShortName = "height")] + public int ImageHeight; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Resizing method", ShortName = "scale")] + public ResizingKind Resizing = ResizingKind.IsoCrop; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Anchor for cropping", ShortName = "anchor")] + public Anchor CropAnchor = Anchor.Center; + } + + /// + /// Extra information for each column (in addition to ColumnInfo). + /// + private sealed class ColInfoEx + { + public readonly int Width; + public readonly int Height; + public readonly ResizingKind Scale; + public readonly Anchor Anchor; + public readonly ColumnType Type; + + public ColInfoEx(int width, int height, ResizingKind scale, Anchor anchor) + { + Contracts.CheckUserArg(width > 0, nameof(Column.ImageWidth)); + Contracts.CheckUserArg(height > 0, nameof(Column.ImageHeight)); + Contracts.CheckUserArg(Enum.IsDefined(typeof(ResizingKind), scale), nameof(Column.Resizing)); + Contracts.CheckUserArg(Enum.IsDefined(typeof(Anchor), anchor), nameof(Column.CropAnchor)); + + Width = width; + Height = height; + Scale = scale; + Anchor = anchor; + Type = new ImageType(Height, Width); + } + } + + internal const string Summary = "Scales an image to specified dimensions using one of the three scale types: isotropic with padding, " + + "isotropic with cropping or anisotropic. In case of isotropic padding, transparent color is used to pad resulting image."; + + internal const string UserName = "Image Resizer Transform"; + public const string LoaderSignature = "ImageScalerTransform"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "IMGSCALF", + //verWrittenCur: 0x00010001, // Initial + verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap + verReadableCur: 0x00010002, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private const string RegistrationName = "ImageScaler"; + + // This is parallel to Infos. + private readonly ColInfoEx[] _exes; + + // Public constructor corresponding to SignatureDataTransform. + public ImageResizerTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, env.CheckRef(args, nameof(args)).Column, input, t => t is ImageType ? null : "Expected Image type") + { + Host.AssertNonEmpty(Infos); + Host.Assert(Infos.Length == Utils.Size(args.Column)); + + _exes = new ColInfoEx[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + { + var item = args.Column[i]; + _exes[i] = new ColInfoEx( + item.ImageWidth ?? args.ImageWidth, + item.ImageHeight ?? args.ImageHeight, + item.Resizing ?? args.Resizing, + item.CropAnchor ?? args.CropAnchor); + } + Metadata.Seal(); + } + + private ImageResizerTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, t => t is ImageType ? null : "Expected Image type") + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + // + // for each added column + // int: width + // int: height + // byte: scaling kind + Host.AssertNonEmpty(Infos); + + _exes = new ColInfoEx[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + { + int width = ctx.Reader.ReadInt32(); + Host.CheckDecode(width > 0); + int height = ctx.Reader.ReadInt32(); + Host.CheckDecode(height > 0); + var scale = (ResizingKind)ctx.Reader.ReadByte(); + Host.CheckDecode(Enum.IsDefined(typeof(ResizingKind), scale)); + var anchor = (Anchor)ctx.Reader.ReadByte(); + Host.CheckDecode(Enum.IsDefined(typeof(Anchor), anchor)); + _exes[i] = new ColInfoEx(width, height, scale, anchor); + } + Metadata.Seal(); + } + + public static ImageResizerTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + ctx.CheckAtModel(GetVersionInfo()); + return h.Apply("Loading Model", + ch => + { + // *** Binary format *** + // int: sizeof(Float) + // + int cbFloat = ctx.Reader.ReadInt32(); + ch.CheckDecode(cbFloat == sizeof(Single)); + return new ImageResizerTransform(h, ctx, input); + }); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Float) + // + // for each added column + // int: width + // int: height + // byte: scaling kind + ctx.Writer.Write(sizeof(Single)); + SaveBase(ctx); + + Host.Assert(_exes.Length == Infos.Length); + for (int i = 0; i < _exes.Length; i++) + { + var ex = _exes[i]; + ctx.Writer.Write(ex.Width); + ctx.Writer.Write(ex.Height); + Host.Assert((ResizingKind)(byte)ex.Scale == ex.Scale); + ctx.Writer.Write((byte)ex.Scale); + Host.Assert((Anchor)(byte)ex.Anchor == ex.Anchor); + ctx.Writer.Write((byte)ex.Anchor); + } + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Check(0 <= iinfo && iinfo < Infos.Length); + return _exes[iinfo].Type; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValueOrNull(ch); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + + var src = default(Bitmap); + var getSrc = GetSrcGetter(input, iinfo); + var ex = _exes[iinfo]; + + disposer = + () => + { + if (src != null) + { + src.Dispose(); + src = null; + } + }; + + ValueGetter del = + (ref Bitmap dst) => + { + if (dst != null) + dst.Dispose(); + + getSrc(ref src); + if (src == null || src.Height <= 0 || src.Width <= 0) + return; + if (src.Height == ex.Height && src.Width == ex.Width) + { + dst = src; + return; + } + + int sourceWidth = src.Width; + int sourceHeight = src.Height; + int sourceX = 0; + int sourceY = 0; + int destX = 0; + int destY = 0; + int destWidth = 0; + int destHeight = 0; + float aspect = 0; + float widthAspect = 0; + float heightAspect = 0; + + widthAspect = (float)ex.Width / sourceWidth; + heightAspect = (float)ex.Height / sourceHeight; + + if (ex.Scale == ResizingKind.IsoPad) + { + widthAspect = (float)ex.Width / sourceWidth; + heightAspect = (float)ex.Height / sourceHeight; + if (heightAspect < widthAspect) + { + aspect = heightAspect; + destX = (int)((ex.Width - (sourceWidth * aspect)) / 2); + } + else + { + aspect = widthAspect; + destY = (int)((ex.Height - (sourceHeight * aspect)) / 2); + } + + destWidth = (int)(sourceWidth * aspect); + destHeight = (int)(sourceHeight * aspect); + } + else + { + if (heightAspect < widthAspect) + { + aspect = widthAspect; + switch (ex.Anchor) + { + case Anchor.Top: + destY = 0; + break; + case Anchor.Bottom: + destY = (int)(ex.Height - (sourceHeight * aspect)); + break; + default: + destY = (int)((ex.Height - (sourceHeight * aspect)) / 2); + break; + } + } + else + { + aspect = heightAspect; + switch (ex.Anchor) + { + case Anchor.Left: + destX = 0; + break; + case Anchor.Right: + destX = (int)(ex.Width - (sourceWidth * aspect)); + break; + default: + destX = (int)((ex.Width - (sourceWidth * aspect)) / 2); + break; + } + } + + destWidth = (int)(sourceWidth * aspect); + destHeight = (int)(sourceHeight * aspect); + } + dst = new Bitmap(ex.Width, ex.Height); + var srcRectangle = new Rectangle(sourceX, sourceY, sourceWidth, sourceHeight); + var destRectangle = new Rectangle(destX, destY, destWidth, destHeight); + using (var g = Graphics.FromImage(dst)) + { + g.DrawImage(src, destRectangle, srcRectangle, GraphicsUnit.Pixel); + } + Host.Assert(dst.Width == ex.Width && dst.Height == ex.Height); + }; + + return del; + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/ImageType.cs b/src/Microsoft.ML.ImageAnalytics/ImageType.cs new file mode 100644 index 0000000000..852ea09d9d --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/ImageType.cs @@ -0,0 +1,58 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + public sealed class ImageType : StructuredType + { + public readonly int Height; + public readonly int Width; + public ImageType(int height, int width) + : base(typeof(Bitmap)) + { + Contracts.CheckParam(height > 0, nameof(height), "Must be positive."); + Contracts.CheckParam(width > 0, nameof(width), " Must be positive."); + Contracts.CheckParam((long)height * width <= int.MaxValue / 4, nameof(height), nameof(height) + " * " + nameof(width) + " is too large."); + Height = height; + Width = width; + } + + public ImageType() : base(typeof(Bitmap)) + { + } + + public override bool Equals(ColumnType other) + { + if (other == this) + return true; + if (!(other is ImageType tmp)) + return false; + if (Height != tmp.Height) + return false; + return Width != tmp.Width; + } + + public override bool Equals(object other) + { + return other is ColumnType tmp && Equals(tmp); + } + + public override int GetHashCode() + { + return Hashing.CombineHash(Height.GetHashCode(), Width.GetHashCode()); + } + + public override string ToString() + { + if (Height == 0 && Width == 0) + return "Image"; + return string.Format("Image<{0}, {1}>", Height, Width); + } + } +} diff --git a/src/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.csproj b/src/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.csproj new file mode 100644 index 0000000000..9829bcf26f --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/Microsoft.ML.ImageAnalytics.csproj @@ -0,0 +1,19 @@ + + + + netstandard2.0 + Microsoft.ML.Runtime.ImageAnalytics + Microsoft.ML.Runtime.ImageAnalytics + Microsoft.ML.ImageAnalytics + + + + + + + + + + + + diff --git a/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs new file mode 100644 index 0000000000..b9d35a6cdc --- /dev/null +++ b/src/Microsoft.ML.ImageAnalytics/VectorToImageTransform.cs @@ -0,0 +1,419 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Drawing; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.ImageAnalytics; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(VectorToImageTransform.Summary, typeof(VectorToImageTransform), typeof(VectorToImageTransform.Arguments), + typeof(SignatureDataTransform), VectorToImageTransform.UserName, "VectorToImageTransform", "VectorToImage")] + +[assembly: LoadableClass(VectorToImageTransform.Summary, typeof(VectorToImageTransform), null, typeof(SignatureLoadDataTransform), + VectorToImageTransform.UserName, VectorToImageTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.ImageAnalytics +{ + // REVIEW: Rewrite as LambdaTransform to simplify. + + /// + /// Transform which takes one or many columns with vectors in them and transform them to representation. + /// + public sealed class VectorToImageTransform : OneToOneTransformBase + { + public class Column : OneToOneColumn + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] + public bool? ContainsAlpha; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] + public bool? ContainsRed; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] + public bool? ContainsGreen; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] + public bool? ContainsBlue; + + // REVIEW: Consider turning this into an enum that allows for pixel, line, or planar interleaving. + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in ARGB order", ShortName = "interleave")] + public bool? InterleaveArgb; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Width of the image", ShortName = "width")] + public int? ImageWidth; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Height of the image", ShortName = "height")] + public int? ImageHeight; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] + public Single? Offset; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] + public Single? Scale; + + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + if (ContainsAlpha != null || ContainsRed != null || ContainsGreen != null || ContainsBlue != null || ImageWidth != null || + ImageHeight != null || Offset != null || Scale != null || InterleaveArgb != null) + { + return false; + } + return TryUnparseCore(sb); + } + } + + public class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 1)] + public Column[] Column; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use alpha channel", ShortName = "alpha")] + public bool ContainsAlpha = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use red channel", ShortName = "red")] + public bool ContainsRed = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use green channel", ShortName = "green")] + public bool ContainsGreen = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to use blue channel", ShortName = "blue")] + public bool ContainsBlue = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to separate each channel or interleave in ARGB order", ShortName = "interleave")] + public bool InterleaveArgb = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Width of the image", ShortName = "width")] + public int ImageWidth; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Height of the image", ShortName = "height")] + public int ImageHeight; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Offset (pre-scale)")] + public Single? Offset; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Scale factor")] + public Single? Scale; + } + + /// + /// Which color channels are extracted. Note that these values are serialized so should not be modified. + /// + [Flags] + private enum ColorBits : byte + { + Alpha = 0x01, + Red = 0x02, + Green = 0x04, + Blue = 0x08, + + All = Alpha | Red | Green | Blue + } + + private sealed class ColInfoEx + { + public readonly ColorBits Colors; + public readonly byte Planes; + + public readonly int Width; + public readonly int Height; + public readonly Single Offset; + public readonly Single Scale; + public readonly bool Interleave; + + public bool Alpha { get { return (Colors & ColorBits.Alpha) != 0; } } + public bool Red { get { return (Colors & ColorBits.Red) != 0; } } + public bool Green { get { return (Colors & ColorBits.Green) != 0; } } + public bool Blue { get { return (Colors & ColorBits.Blue) != 0; } } + + public ColInfoEx(Column item, Arguments args) + { + if (item.ContainsAlpha ?? args.ContainsAlpha) { Colors |= ColorBits.Alpha; Planes++; } + if (item.ContainsRed ?? args.ContainsRed) { Colors |= ColorBits.Red; Planes++; } + if (item.ContainsGreen ?? args.ContainsGreen) { Colors |= ColorBits.Green; Planes++; } + if (item.ContainsBlue ?? args.ContainsBlue) { Colors |= ColorBits.Blue; Planes++; } + Contracts.CheckUserArg(Planes > 0, nameof(item.ContainsRed), "Need to use at least one color plane"); + + Interleave = item.InterleaveArgb ?? args.InterleaveArgb; + + Width = item.ImageWidth ?? args.ImageWidth; + Height = item.ImageHeight ?? args.ImageHeight; + Offset = item.Offset ?? args.Offset ?? 0; + Scale = item.Scale ?? args.Scale ?? 1; + Contracts.CheckUserArg(FloatUtils.IsFinite(Offset), nameof(item.Offset)); + Contracts.CheckUserArg(FloatUtils.IsFiniteNonZero(Scale), nameof(item.Scale)); + } + + public ColInfoEx(ModelLoadContext ctx) + { + Contracts.AssertValue(ctx); + + // *** Binary format *** + // byte: colors + // int: widht + // int: height + // Float: offset + // Float: scale + // byte: separateChannels + Colors = (ColorBits)ctx.Reader.ReadByte(); + Contracts.CheckDecode(Colors != 0); + Contracts.CheckDecode((Colors & ColorBits.All) == Colors); + + // Count the planes. + int planes = (int)Colors; + planes = (planes & 0x05) + ((planes >> 1) & 0x05); + planes = (planes & 0x03) + ((planes >> 2) & 0x03); + Planes = (byte)planes; + Contracts.Assert(0 < Planes & Planes <= 4); + + Width = ctx.Reader.ReadInt32(); + Contracts.CheckDecode(Width > 0); + Height = ctx.Reader.ReadInt32(); + Contracts.CheckDecode(Height > 0); + Offset = ctx.Reader.ReadFloat(); + Contracts.CheckDecode(FloatUtils.IsFinite(Offset)); + Scale = ctx.Reader.ReadFloat(); + Contracts.CheckDecode(FloatUtils.IsFiniteNonZero(Scale)); + Interleave = ctx.Reader.ReadBoolByte(); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.AssertValue(ctx); + +#if DEBUG + // This code is used in deserialization - assert that it matches what we computed above. + int planes = (int)Colors; + planes = (planes & 0x05) + ((planes >> 1) & 0x05); + planes = (planes & 0x03) + ((planes >> 2) & 0x03); + Contracts.Assert(planes == Planes); +#endif + + // *** Binary format *** + // byte: colors + // byte: convert + // Float: offset + // Float: scale + // byte: separateChannels + Contracts.Assert(Colors != 0); + Contracts.Assert((Colors & ColorBits.All) == Colors); + ctx.Writer.Write((byte)Colors); + ctx.Writer.Write(Width); + ctx.Writer.Write(Height); + Contracts.Assert(FloatUtils.IsFinite(Offset)); + ctx.Writer.Write(Offset); + Contracts.Assert(FloatUtils.IsFiniteNonZero(Scale)); + ctx.Writer.Write(Scale); + ctx.Writer.WriteBoolByte(Interleave); + } + } + + public const string Summary = "Converts vector array into image type."; + public const string UserName = "Vector To Image Transform"; + public const string LoaderSignature = "VectorToImageConverter"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "VECTOIMG", + //verWrittenCur: 0x00010001, // Initial + verWrittenCur: 0x00010002, // Swith from OpenCV to Bitmap + verReadableCur: 0x00010002, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private const string RegistrationName = "VectorToImageConverter"; + + private readonly ColInfoEx[] _exes; + private readonly ImageType[] _types; + + // Public constructor corresponding to SignatureDataTransform. + public VectorToImageTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, Contracts.CheckRef(args, nameof(args)).Column, input, + t => t is VectorType ? null : "Expected VectorType type") + { + Host.AssertNonEmpty(Infos); + Host.Assert(Infos.Length == Utils.Size(args.Column)); + + _exes = new ColInfoEx[Infos.Length]; + _types = new ImageType[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + { + var item = args.Column[i]; + _exes[i] = new ColInfoEx(item, args); + _types[i] = new ImageType(_exes[i].Height, _exes[i].Width); + } + Metadata.Seal(); + } + + private VectorToImageTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, t => t is VectorType ? null : "Expected VectorType type") + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + // + // foreach added column + // ColInfoEx + Host.AssertNonEmpty(Infos); + _exes = new ColInfoEx[Infos.Length]; + _types = new ImageType[Infos.Length]; + for (int i = 0; i < _exes.Length; i++) + { + _exes[i] = new ColInfoEx(ctx); + _types[i] = new ImageType(_exes[i].Height, _exes[i].Width); + } + Metadata.Seal(); + } + + public static VectorToImageTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + ctx.CheckAtModel(GetVersionInfo()); + + return h.Apply("Loading Model", + ch => + { + // *** Binary format *** + // int: sizeof(Float) + // + int cbFloat = ctx.Reader.ReadInt32(); + ch.CheckDecode(cbFloat == sizeof(Single)); + return new VectorToImageTransform(h, ctx, input); + }); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Float) + // + // foreach added column + // ColInfoEx + ctx.Writer.Write(sizeof(Single)); + SaveBase(ctx); + + Host.Assert(_exes.Length == Infos.Length); + for (int i = 0; i < _exes.Length; i++) + _exes[i].Save(ctx); + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Assert(0 <= iinfo & iinfo < Infos.Length); + return _types[iinfo]; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValueOrNull(ch); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + + var type = _types[iinfo]; + var ex = _exes[iinfo]; + bool needScale = ex.Offset != 0 || ex.Scale != 1; + disposer = null; + var sourceType = Schema.GetColumnType(Infos[iinfo].Source); + if (sourceType.ItemType == NumberType.R4 || sourceType.ItemType == NumberType.R8) + return GetterFromType(input, iinfo, ex, needScale); + else + if (sourceType.ItemType == NumberType.U1) + return GetterFromType(input, iinfo, ex, false); + else + throw Contracts.Except("We only support float or byte arrays"); + + } + + private ValueGetter GetterFromType(IRow input, int iinfo, ColInfoEx ex, bool needScale) where TValue : IConvertible + { + var getSrc = GetSrcGetter>(input, iinfo); + var src = default(VBuffer); + int width = ex.Width; + int height = ex.Height; + float offset = ex.Offset; + float scale = ex.Scale; + + return + (ref Bitmap dst) => + { + getSrc(ref src); + if (src.Count == 0) + { + dst = null; + return; + } + VBuffer dense = default; + src.CopyToDense(ref dense); + var values = dense.Values; + dst = new Bitmap(width, height); + dst.SetResolution(width, height); + int cpix = height * width; + int planes = dense.Count / cpix; + int position = 0; + + for (int x = 0; x < width; x++) + for (int y = 0; y < height; ++y) + { + float red = 0; + float green = 0; + float blue = 0; + float alpha = 0; + if (ex.Interleave) + { + if (ex.Alpha) position++; + if (ex.Red) red = Convert.ToSingle(values[position++]); + if (ex.Green) green = Convert.ToSingle(values[position++]); + if (ex.Blue) blue = Convert.ToSingle(values[position++]); + } + else + { + position = y * width + x; + if (ex.Alpha) { alpha = Convert.ToSingle(values[position]); position += cpix; } + if (ex.Red) { red = Convert.ToSingle(values[position]); position += cpix; } + if (ex.Green) { green = Convert.ToSingle(values[position]); position += cpix; } + if (ex.Blue) { blue = Convert.ToSingle(values[position]); position += cpix; } + } + Color pixel; + if (!needScale) + pixel = Color.FromArgb((int)alpha, (int)red, (int)green, (int)blue); + else + { + pixel = Color.FromArgb( + (int)((alpha - offset) * scale), + (int)((red - offset) * scale), + (int)((green - offset) * scale), + (int)((blue - offset) * scale)); + } + dst.SetPixel(x, y, pixel); + } + }; + } + } +} + diff --git a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs index 1ab65a8cd5..3ef977ba35 100644 --- a/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs +++ b/src/Microsoft.ML.KMeansClustering/KMeansPlusPlusTrainer.cs @@ -830,7 +830,7 @@ public static void Initialize(IHost host, int numThreads, IChannel ch, FeatureFl { weights = new Float[totalSamples]; for (int i = 0; i < workStateWeights.Length; i++) - SseUtils.Add(workStateWeights[i], weights, totalSamples); + CpuMathUtils.Add(workStateWeights[i], weights, totalSamples); }, ref weightBuffer, ref totalWeights); #if DEBUG diff --git a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs index 9c40e61ca2..b68f22b919 100644 --- a/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs +++ b/src/Microsoft.ML.Onnx/SaveOnnxCommand.cs @@ -28,7 +28,7 @@ public sealed class SaveOnnxCommand : DataCommand.ImplBase + netstandard2.0 diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs index 04d3e2d9e4..cfda92c503 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearClassificationTrainer.cs @@ -207,10 +207,9 @@ internal virtual void Check(IHostEnvironment env) { using (var ch = env.Start("SDCA arguments checking")) { - ch.Warning("The specified l2Const = {0} is too small. SDCA optimizes the dual objective function. " + - "The dual formulation is only valid with a positive L2 regularization. Also, an l2Const less than {1} " + - "could drastically slow down the convergence. So using l2Const = {1} instead.", L2Const); - + ch.Warning($"The L2 regularization constant must be at least {L2LowerBound}. In SDCA, the dual formulation " + + $"is only valid with a positive constant, and values below {L2LowerBound} cause very slow convergence. " + + $"The original {nameof(L2Const)} = {L2Const}, was replaced with {nameof(L2Const)} = {L2LowerBound}."); L2Const = L2LowerBound; ch.Done(); } @@ -798,9 +797,9 @@ protected virtual void TrainWithoutLock(IProgressChannelProvider progress, Float } if (features.IsDense) - SseUtils.SdcaL1UpdateDense(primalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[0].Values, weights[0].Values); + CpuMathUtils.SdcaL1UpdateDense(primalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[0].Values, weights[0].Values); else if (features.Count > 0) - SseUtils.SdcaL1UpdateSparse(primalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[0].Values, weights[0].Values); + CpuMathUtils.SdcaL1UpdateSparse(primalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[0].Values, weights[0].Values); } break; @@ -1752,4 +1751,4 @@ public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironm } } -} \ No newline at end of file +} diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs index 8f81324768..82069d413d 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs @@ -553,7 +553,7 @@ public override void SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICali public abstract class RegressionPredictor : LinearPredictor { - internal RegressionPredictor(IHostEnvironment env, string name, ref VBuffer weights, Float bias) + protected RegressionPredictor(IHostEnvironment env, string name, ref VBuffer weights, Float bias) : base(env, name, ref weights, bias) { } diff --git a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs index 09e2bbbcc4..47b08c586a 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs @@ -28,7 +28,6 @@ namespace Microsoft.ML.Runtime.Learners { - using Mkl = Microsoft.ML.Runtime.Learners.OlsLinearRegressionTrainer.Mkl; /// /// @@ -282,64 +281,7 @@ protected override void ComputeTrainingStatistics(IChannel ch, FloatLabelCursor. } } - // Apply Cholesky Decomposition to find the inverse of the Hessian. - Double[] invHessian = null; - try - { - // First, find the Cholesky decomposition LL' of the Hessian. - Mkl.Pptrf(Mkl.Layout.RowMajor, Mkl.UpLo.Lo, numParams, hessian); - // Note that hessian is already modified at this point. It is no longer the original Hessian, - // but instead represents the Cholesky decomposition L. - // Also note that the following routine is supposed to consume the Cholesky decomposition L instead - // of the original information matrix. - Mkl.Pptri(Mkl.Layout.RowMajor, Mkl.UpLo.Lo, numParams, hessian); - // At this point, hessian should contain the inverse of the original Hessian matrix. - // Swap hessian with invHessian to avoid confusion in the following context. - Utils.Swap(ref hessian, ref invHessian); - Contracts.Assert(hessian == null); - } - catch (DllNotFoundException) - { - throw ch.ExceptNotSupp("The MKL library (Microsoft.ML.MklImports.dll) or one of its dependencies is missing."); - } - - Float[] stdErrorValues = new Float[numParams]; - stdErrorValues[0] = (Float)Math.Sqrt(invHessian[0]); - - for (int i = 1; i < numParams; i++) - { - // Initialize with inverse Hessian. - stdErrorValues[i] = (Single)invHessian[i * (i + 1) / 2 + i]; - } - - if (L2Weight > 0) - { - // Iterate through all entries of inverse Hessian to make adjustment to variance. - // A discussion on ridge regularized LR coefficient covariance matrix can be found here: - // http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3228544/ - // http://www.inf.unibz.it/dis/teaching/DWDM/project2010/LogisticRegression.pdf - int ioffset = 1; - for (int iRow = 1; iRow < numParams; iRow++) - { - for (int iCol = 0; iCol <= iRow; iCol++) - { - var entry = (Single)invHessian[ioffset]; - var adjustment = -L2Weight * entry * entry; - stdErrorValues[iRow] -= adjustment; - if (0 < iCol && iCol < iRow) - stdErrorValues[iCol] -= adjustment; - ioffset++; - } - } - - Contracts.Assert(ioffset == invHessian.Length); - } - - for (int i = 1; i < numParams; i++) - stdErrorValues[i] = (Float)Math.Sqrt(stdErrorValues[i]); - - VBuffer stdErrors = new VBuffer(CurrentWeights.Length, numParams, stdErrorValues, weightIndices); - _stats = new LinearModelStatistics(Host, NumGoodRows, numParams, deviance, nullDeviance, ref stdErrors); + _stats = new LinearModelStatistics(Host, NumGoodRows, numParams, deviance, nullDeviance); } protected override void ProcessPriorDistribution(Float label, Float weight) @@ -382,7 +324,7 @@ protected override ParameterMixingCalibratedPredictor CreatePredictor() CurrentWeights.GetItemOrDefault(0, ref bias); CurrentWeights.CopyTo(ref weights, 1, CurrentWeights.Length - 1); return new ParameterMixingCalibratedPredictor(Host, - new LinearBinaryPredictor(Host, ref weights, bias, _stats), + new LinearBinaryPredictor(Host, ref weights, bias), new PlattCalibrator(Host, -1, 0)); } diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs index 52cd025370..05aee405ca 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/MetaMulticlassTrainer.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.Conversion; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Internallearn; using Microsoft.ML.Runtime.Training; @@ -21,13 +22,12 @@ public abstract class MetaMulticlassTrainer : TrainerBase { public abstract class ArgumentsBase { - [Argument(ArgumentType.Multiple, HelpText = "Base predictor", ShortName = "p", SortOrder = 1)] + [Argument(ArgumentType.Multiple, HelpText = "Base predictor", ShortName = "p", SortOrder = 1, SignatureType = typeof(SignatureBinaryClassifierTrainer))] [TGUI(Label = "Predictor Type", Description = "Type of underlying binary predictor")] - public SubComponent PredictorType = - new SubComponent(LinearSvm.LoadNameValue); + public IComponentFactory PredictorType; - [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "")] - public SubComponent Calibrator = new SubComponent("PlattCalibration"); + [Argument(ArgumentType.Multiple, HelpText = "Output calibrator", ShortName = "cali", NullName = "", SignatureType = typeof(SignatureCalibrator))] + public IComponentFactory Calibrator = new PlattCalibratorTrainerFactory(); [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of instances to train the calibrator", ShortName = "numcali")] public int MaxCalibrationExamples = 1000000000; @@ -47,14 +47,20 @@ internal MetaMulticlassTrainer(IHostEnvironment env, TArgs args, string name) { Host.CheckValue(args, nameof(args)); Args = args; - Host.CheckUserArg(Args.PredictorType.IsGood(), nameof(Args.PredictorType)); // Create the first trainer so errors in the args surface early. - _trainer = Args.PredictorType.CreateInstance(Host); + _trainer = CreateTrainer(); // Regarding caching, no matter what the internal predictor, we're performing many passes // simply by virtue of this being a meta-trainer, so we will still cache. Info = new TrainerInfo(normalization: _trainer.Info.NeedNormalization); } + private TScalarTrainer CreateTrainer() + { + return Args.PredictorType != null ? + Args.PredictorType.CreateComponent(Host) : + new LinearSvm(Host, new LinearSvm.Arguments()); + } + protected IDataView MapLabelsCore(ColumnType type, RefPredicate equalsTarget, RoleMappedData data, string dstName) { Host.AssertValue(type); @@ -84,7 +90,7 @@ protected TScalarTrainer GetTrainer() { // We may have instantiated the first trainer to use already, from the constructor. // If so capture it and set the retained trainer to null; otherwise create a new one. - var train = _trainer ?? Args.PredictorType.CreateInstance(Host); + var train = _trainer ?? CreateTrainer(); _trainer = null; return train; } diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs index c123411edd..24359feca3 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs @@ -89,10 +89,10 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe if (Args.UseProbabilities) { ICalibratorTrainer calibrator; - if (!Args.Calibrator.IsGood()) + if (Args.Calibrator == null) calibrator = null; else - calibrator = Args.Calibrator.CreateInstance(Host); + calibrator = Args.Calibrator.CreateComponent(Host); var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples, trainer, predictor, td); predictor = res as TScalarPredictor; diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs index 193c8f0290..6434038384 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Pkpd.cs @@ -104,10 +104,10 @@ private TDistPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappedD var predictor = trainer.Train(td); ICalibratorTrainer calibrator; - if (!Args.Calibrator.IsGood()) + if (Args.Calibrator == null) calibrator = null; else - calibrator = Args.Calibrator.CreateInstance(Host); + calibrator = Args.Calibrator.CreateComponent(Host); var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples, trainer, predictor, td); var dist = res as TDistPredictor; diff --git a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs index 60fe7f9705..4976bf20d3 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/Online/OnlineLinear.cs @@ -84,7 +84,7 @@ protected OnlineLinearTrainer(TArguments args, IHostEnvironment env, string name Args = args; // REVIEW: Caching could be false for one iteration, if we got around the whole shuffling issue. - Info = new TrainerInfo(calibration: NeedCalibration); + Info = new TrainerInfo(calibration: NeedCalibration, supportIncrementalTrain: true); } /// diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs index af30335af6..5bfc40785a 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs @@ -175,9 +175,9 @@ protected override void TrainWithoutLock(IProgressChannelProvider progress, Floa } if (features.IsDense) - SseUtils.SdcaL1UpdateDense(-primalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values); + CpuMathUtils.SdcaL1UpdateDense(-primalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values); else if (features.Count > 0) - SseUtils.SdcaL1UpdateSparse(-primalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values); + CpuMathUtils.SdcaL1UpdateSparse(-primalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[iClass].Values, weights[iClass].Values); } break; @@ -202,9 +202,9 @@ protected override void TrainWithoutLock(IProgressChannelProvider progress, Floa : 0; if (features.IsDense) - SseUtils.SdcaL1UpdateDense(labelPrimalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values); + CpuMathUtils.SdcaL1UpdateDense(labelPrimalUpdate, features.Length, features.Values, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values); else if (features.Count > 0) - SseUtils.SdcaL1UpdateSparse(labelPrimalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values); + CpuMathUtils.SdcaL1UpdateSparse(labelPrimalUpdate, features.Length, features.Values, features.Indices, features.Count, l1Threshold, l1IntermediateWeights[label].Values, weights[label].Values); } rowCount++; diff --git a/src/Microsoft.ML.StandardLearners/Standard/doc.xml b/src/Microsoft.ML.StandardLearners/Standard/doc.xml index eb87605232..a3fe4a592a 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/doc.xml +++ b/src/Microsoft.ML.StandardLearners/Standard/doc.xml @@ -21,7 +21,7 @@ Elastic net regularization can be specified by the 'L2Const' and 'L1Threshold' parameters. Note that the 'L2Const' has an effect on the rate of convergence. In general, the larger the 'L2Const', the faster SDCA converges. - For more information, see also: + For more information, see: Scaling Up Stochastic Dual Coordinate Ascent. diff --git a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs index 543b997e6d..3d29bce613 100644 --- a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs +++ b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs @@ -137,5 +137,25 @@ public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, LdaTr OutputData = view }; } + + [TlcModule.EntryPoint(Name = "Transforms.WordEmbeddings", + Desc = WordEmbeddingsTransform.Summary, + UserName = WordEmbeddingsTransform.UserName, + ShortName = WordEmbeddingsTransform.ShortName, + XmlInclude = new[] { @"", + @"" })] + public static CommonOutputs.TransformOutput WordEmbeddings(IHostEnvironment env, WordEmbeddingsTransform.Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(input, nameof(input)); + + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "WordEmbeddings", input); + var view = new WordEmbeddingsTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, view, input.Data), + OutputData = view + }; + } } } diff --git a/src/Microsoft.ML.Transforms/GcnTransform.cs b/src/Microsoft.ML.Transforms/GcnTransform.cs index 6edfdfb4a2..c9b8398814 100644 --- a/src/Microsoft.ML.Transforms/GcnTransform.cs +++ b/src/Microsoft.ML.Transforms/GcnTransform.cs @@ -565,7 +565,7 @@ private static void FillValues(IExceptionContext ectx, ref VBuffer src, r Array.Copy(src.Indices, dstIndices, count); } - SseUtils.Scale(normScale, src.Values, dstValues, count); + CpuMathUtils.Scale(normScale, src.Values, dstValues, count); dst = new VBuffer(length, count, dstValues, dstIndices); return; @@ -575,9 +575,9 @@ private static void FillValues(IExceptionContext ectx, ref VBuffer src, r src.CopyToDense(ref dst); if (normScale != 1) - SseUtils.ScaleAdd(normScale, -offset, dst.Values, length); + CpuMathUtils.ScaleAdd(normScale, -offset, dst.Values, length); else - SseUtils.Add(-offset, dst.Values, length); + CpuMathUtils.Add(-offset, dst.Values, length); } /// @@ -591,7 +591,7 @@ private static Float StdDev(Float[] values, int count, int length) if (count == 0) return 0; // We need a mean to compute variance. - Float tmpMean = SseUtils.Sum(values, 0, count) / length; + Float tmpMean = CpuMathUtils.Sum(values, 0, count) / length; Float sumSq = 0; if (count != length && tmpMean != 0) { @@ -599,7 +599,7 @@ private static Float StdDev(Float[] values, int count, int length) Float meanSq = tmpMean * tmpMean; sumSq = (length - count) * meanSq; } - sumSq += SseUtils.SumSq(tmpMean, values, 0, count); + sumSq += CpuMathUtils.SumSq(tmpMean, values, 0, count); return MathUtils.Sqrt(sumSq / length); } @@ -619,7 +619,7 @@ private static Float StdDev(Float[] values, int count, int length, Float mean) Float meanSq = mean * mean; sumSq = (length - count) * meanSq; } - sumSq += SseUtils.SumSq(mean, values, 0, count); + sumSq += CpuMathUtils.SumSq(mean, values, 0, count); return MathUtils.Sqrt(sumSq / length); } @@ -631,7 +631,7 @@ private static Float L2Norm(Float[] values, int count, Float mean = 0) { if (count == 0) return 0; - return MathUtils.Sqrt(SseUtils.SumSq(mean, values, 0, count)); + return MathUtils.Sqrt(CpuMathUtils.SumSq(mean, values, 0, count)); } /// @@ -642,7 +642,7 @@ private static Float L1Norm(Float[] values, int count, Float mean = 0) { if (count == 0) return 0; - return SseUtils.SumAbs(mean, values, 0, count); + return CpuMathUtils.SumAbs(mean, values, 0, count); } /// @@ -653,14 +653,14 @@ private static Float LInfNorm(Float[] values, int count, Float mean = 0) { if (count == 0) return 0; - return SseUtils.MaxAbsDiff(mean, values, count); + return CpuMathUtils.MaxAbsDiff(mean, values, count); } private static Float Mean(Float[] src, int count, int length) { if (length == 0 || count == 0) return 0; - return SseUtils.Sum(src, 0, count) / length; + return CpuMathUtils.Sum(src, 0, count) / length; } } diff --git a/src/Microsoft.ML.Transforms/HashJoinTransform.cs b/src/Microsoft.ML.Transforms/HashJoinTransform.cs index 4f72760c99..8120ffc078 100644 --- a/src/Microsoft.ML.Transforms/HashJoinTransform.cs +++ b/src/Microsoft.ML.Transforms/HashJoinTransform.cs @@ -152,8 +152,7 @@ private static KeyType GetItemType(int hashBits) internal const string RegistrationName = "HashJoin"; - internal const string Summary = "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. " - + "This is a part of the Dracula transform."; + internal const string Summary = "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. "; internal const string UserName = "Hash Join Transform"; diff --git a/src/Microsoft.ML.Transforms/RffTransform.cs b/src/Microsoft.ML.Transforms/RffTransform.cs index b7a112e954..6ad6ceec5f 100644 --- a/src/Microsoft.ML.Transforms/RffTransform.cs +++ b/src/Microsoft.ML.Transforms/RffTransform.cs @@ -23,8 +23,6 @@ namespace Microsoft.ML.Runtime.Data { - using CpuUtils = SseUtils; - public sealed class RffTransform : OneToOneTransformBase { private static class Defaults @@ -124,8 +122,8 @@ public TransformInfo(IHost host, Column item, Arguments args, int d, Float avgDi int roundedUpD = RoundUp(NewDim, CfltAlign); int roundedUpNumFeatures = RoundUp(SrcDim, CfltAlign); - RndFourierVectors = new AlignedArray(roundedUpD * roundedUpNumFeatures, CpuUtils.CbAlign); - RotationTerms = _useSin ? null : new AlignedArray(roundedUpD, CpuUtils.CbAlign); + RndFourierVectors = new AlignedArray(roundedUpD * roundedUpNumFeatures, CpuMathUtils.Vector128Alignment); + RotationTerms = _useSin ? null : new AlignedArray(roundedUpD, CpuMathUtils.Vector128Alignment); InitializeFourierCoefficients(roundedUpNumFeatures, roundedUpD); } @@ -160,8 +158,8 @@ public TransformInfo(IHostEnvironment env, ModelLoadContext ctx, int colValueCou // initialize the transform matrix int roundedUpD = RoundUp(NewDim, CfltAlign); int roundedUpNumFeatures = RoundUp(SrcDim, CfltAlign); - RndFourierVectors = new AlignedArray(roundedUpD * roundedUpNumFeatures, CpuUtils.CbAlign); - RotationTerms = _useSin ? null : new AlignedArray(roundedUpD, CpuUtils.CbAlign); + RndFourierVectors = new AlignedArray(roundedUpD * roundedUpNumFeatures, CpuMathUtils.Vector128Alignment); + RotationTerms = _useSin ? null : new AlignedArray(roundedUpD, CpuMathUtils.Vector128Alignment); InitializeFourierCoefficients(roundedUpNumFeatures, roundedUpD); } @@ -229,7 +227,7 @@ private static VersionInfo GetVersionInfo() private readonly TransformInfo[] _transformInfos; private const string RegistrationName = "Rff"; - private const int CfltAlign = CpuUtils.CbAlign / sizeof(float); + private const int CfltAlign = CpuMathUtils.Vector128Alignment / sizeof(float); private static string TestColumnType(ColumnType type) { @@ -498,8 +496,8 @@ private ValueGetter> GetterFromVectorType(IRow input, int iinfo) var getSrc = GetSrcGetter>(input, iinfo); var src = default(VBuffer); - var featuresAligned = new AlignedArray(RoundUp(Infos[iinfo].TypeSrc.ValueCount, CfltAlign), CpuUtils.CbAlign); - var productAligned = new AlignedArray(RoundUp(_transformInfos[iinfo].NewDim, CfltAlign), CpuUtils.CbAlign); + var featuresAligned = new AlignedArray(RoundUp(Infos[iinfo].TypeSrc.ValueCount, CfltAlign), CpuMathUtils.Vector128Alignment); + var productAligned = new AlignedArray(RoundUp(_transformInfos[iinfo].NewDim, CfltAlign), CpuMathUtils.Vector128Alignment); return (ref VBuffer dst) => @@ -514,8 +512,8 @@ private ValueGetter> GetterFromFloatType(IRow input, int iinfo) var getSrc = GetSrcGetter(input, iinfo); var src = default(Float); - var featuresAligned = new AlignedArray(RoundUp(1, CfltAlign), CpuUtils.CbAlign); - var productAligned = new AlignedArray(RoundUp(_transformInfos[iinfo].NewDim, CfltAlign), CpuUtils.CbAlign); + var featuresAligned = new AlignedArray(RoundUp(1, CfltAlign), CpuMathUtils.Vector128Alignment); + var productAligned = new AlignedArray(RoundUp(_transformInfos[iinfo].NewDim, CfltAlign), CpuMathUtils.Vector128Alignment); var oneDimensionalVector = new VBuffer(1, new Float[] { 0 }); @@ -552,7 +550,7 @@ private static void TransformFeatures(IHost host, ref VBuffer src, ref VB if (src.IsDense) { featuresAligned.CopyFrom(src.Values, 0, src.Length); - CpuUtils.MatTimesSrc(false, false, transformInfo.RndFourierVectors, featuresAligned, productAligned, + CpuMathUtils.MatTimesSrc(false, false, transformInfo.RndFourierVectors, featuresAligned, productAligned, transformInfo.NewDim); } else @@ -560,7 +558,7 @@ private static void TransformFeatures(IHost host, ref VBuffer src, ref VB // This overload of MatTimesSrc ignores the values in slots that are not in src.Indices, so there is // no need to zero them out. featuresAligned.CopyFrom(src.Indices, src.Values, 0, 0, src.Count, zeroItems: false); - CpuUtils.MatTimesSrc(false, false, transformInfo.RndFourierVectors, src.Indices, featuresAligned, 0, 0, + CpuMathUtils.MatTimesSrc(false, false, transformInfo.RndFourierVectors, src.Indices, featuresAligned, 0, 0, src.Count, productAligned, transformInfo.NewDim); } diff --git a/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs b/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs index fb56fa2e0a..9d6f5564cf 100644 --- a/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs +++ b/src/Microsoft.ML.Transforms/Text/WordBagTransform.cs @@ -451,8 +451,8 @@ public sealed class TermLoaderArguments [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Data file containing the terms", ShortName = "data", SortOrder = 2, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] public string DataFile; - [Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] - public SubComponent Loader; + [Argument(ArgumentType.Multiple, HelpText = "Data loader", NullName = "", SortOrder = 3, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, SignatureType = typeof(SignatureDataLoader))] + public IComponentFactory Loader; [Argument(ArgumentType.AtMostOnce, HelpText = "Name of the text column containing the terms", ShortName = "termCol", SortOrder = 4, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] public string TermsColumn; diff --git a/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs new file mode 100644 index 0000000000..bf85ddf42f --- /dev/null +++ b/src/Microsoft.ML.Transforms/Text/WordEmbeddingsTransform.cs @@ -0,0 +1,444 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(WordEmbeddingsTransform.Summary, typeof(IDataTransform), typeof(WordEmbeddingsTransform), typeof(WordEmbeddingsTransform.Arguments), + typeof(SignatureDataTransform), WordEmbeddingsTransform.UserName, "WordEmbeddingsTransform", WordEmbeddingsTransform.ShortName, DocName = "transform/WordEmbeddingsTransform.md")] + +[assembly: LoadableClass(typeof(WordEmbeddingsTransform), null, typeof(SignatureLoadDataTransform), + WordEmbeddingsTransform.UserName, WordEmbeddingsTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Data +{ + /// + public sealed class WordEmbeddingsTransform : OneToOneTransformBase + { + public sealed class Column : OneToOneColumn + { + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + return TryUnparseCore(sb); + } + } + + public sealed class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:src)", ShortName = "col", SortOrder = 0)] + public Column[] Column; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Pre-trained model used to create the vocabulary", ShortName = "model", SortOrder = 1)] + public PretrainedModelKind? ModelKind = PretrainedModelKind.Sswe; + + [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Filename for custom word embedding model", + ShortName = "dataFile", SortOrder = 2)] + public string CustomLookupTable; + } + + internal const string Summary = "Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence " + + "vectors using a pre-trained model"; + internal const string UserName = "Word Embeddings Transform"; + internal const string ShortName = "WordEmbeddings"; + public const string LoaderSignature = "WordEmbeddingsTransform"; + + public static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "W2VTRANS", + verWrittenCur: 0x00010001, //Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + private readonly PretrainedModelKind? _modelKind; + private readonly string _modelFileNameWithPath; + private readonly Model _currentVocab; + private static object _embeddingsLock = new object(); + private readonly VectorType _outputType; + private readonly bool _customLookup; + private readonly int _linesToSkip; + private static Dictionary> _vocab = new Dictionary>(); + + private sealed class Model + { + private readonly BigArray _wordVectors; + private readonly NormStr.Pool _pool; + public readonly int Dimension; + + public Model(int dimension) + { + Dimension = dimension; + _wordVectors = new BigArray(); + _pool = new NormStr.Pool(); + } + + public void AddWordVector(IChannel ch, string word, float[] wordVector) + { + ch.Assert(wordVector.Length == Dimension); + if (_pool.Get(word) == null) + { + _pool.Add(word); + _wordVectors.AddRange(wordVector, Dimension); + } + } + + public bool GetWordVector(ref DvText word, float[] wordVector) + { + if (word.IsNA) + return false; + string rawWord = word.GetRawUnderlyingBufferInfo(out int ichMin, out int ichLim); + NormStr str = _pool.Get(rawWord, ichMin, ichLim); + if (str != null) + { + _wordVectors.CopyTo(str.Id * Dimension, wordVector, Dimension); + return true; + } + return false; + } + } + + private const string RegistrationName = "WordEmbeddings"; + + private const int Timeout = 10 * 60 * 1000; + + /// + /// Public constructor corresponding to . + /// + public WordEmbeddingsTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, Contracts.CheckRef(args, nameof(args)).Column, + input, TestIsTextVector) + { + if (args.ModelKind == null) + args.ModelKind = PretrainedModelKind.Sswe; + Host.CheckUserArg(!args.ModelKind.HasValue || Enum.IsDefined(typeof(PretrainedModelKind), args.ModelKind), nameof(args.ModelKind)); + Host.AssertNonEmpty(Infos); + Host.Assert(Infos.Length == Utils.Size(args.Column)); + + _customLookup = !string.IsNullOrWhiteSpace(args.CustomLookupTable); + if (_customLookup) + { + _modelKind = null; + _modelFileNameWithPath = args.CustomLookupTable; + } + else + { + _modelKind = args.ModelKind; + _modelFileNameWithPath = EnsureModelFile(env, out _linesToSkip, (PretrainedModelKind)_modelKind); + } + + Host.CheckNonWhiteSpace(_modelFileNameWithPath, nameof(_modelFileNameWithPath)); + _currentVocab = GetVocabularyDictionary(); + _outputType = new VectorType(NumberType.R4, 3 * _currentVocab.Dimension); + Metadata.Seal(); + } + + private WordEmbeddingsTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, TestIsTextVector) + { + Host.AssertValue(ctx); + Host.AssertNonEmpty(Infos); + _customLookup = ctx.Reader.ReadBoolByte(); + + if (_customLookup) + { + _modelFileNameWithPath = ctx.LoadNonEmptyString(); + _modelKind = null; + } + else + { + _modelKind = (PretrainedModelKind)ctx.Reader.ReadUInt32(); + _modelFileNameWithPath = EnsureModelFile(Host, out _linesToSkip, (PretrainedModelKind)_modelKind); + } + + Host.CheckNonWhiteSpace(_modelFileNameWithPath, nameof(_modelFileNameWithPath)); + _currentVocab = GetVocabularyDictionary(); + _outputType = new VectorType(NumberType.R4, 3 * _currentVocab.Dimension); + Metadata.Seal(); + } + + public static WordEmbeddingsTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + IHost h = env.Register(RegistrationName); + h.CheckValue(ctx, nameof(ctx)); + h.CheckValue(input, nameof(input)); + return h.Apply("Loading Model", + ch => new WordEmbeddingsTransform(h, ctx, input)); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + SaveBase(ctx); + ctx.Writer.WriteBoolByte(_customLookup); + if (_customLookup) + ctx.SaveString(_modelFileNameWithPath); + else + ctx.Writer.Write((uint)_modelKind); + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + return _outputType; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValue(ch); + ch.AssertValue(input); + ch.Assert(0 <= iinfo && iinfo < Infos.Length); + disposer = null; + + var info = Infos[iinfo]; + if (!info.TypeSrc.IsVector) + { + throw Host.ExceptParam(nameof(input), + "Text input given, expects a text vector"); + } + return GetGetterVec(ch, input, iinfo); + } + + private ValueGetter> GetGetterVec(IChannel ch, IRow input, int iinfo) + { + Host.AssertValue(ch); + ch.AssertValue(input); + ch.Assert(0 <= iinfo && iinfo < Infos.Length); + + var info = Infos[iinfo]; + ch.Assert(info.TypeSrc.IsVector); + ch.Assert(info.TypeSrc.ItemType.IsText); + + var srcGetter = input.GetGetter>(info.Source); + var src = default(VBuffer); + int dimension = _currentVocab.Dimension; + float[] wordVector = new float[_currentVocab.Dimension]; + + return + (ref VBuffer dst) => + { + int deno = 0; + srcGetter(ref src); + var values = dst.Values; + if (Utils.Size(values) != 3 * dimension) + values = new float[3 * dimension]; + int offset = 2 * dimension; + for (int i = 0; i < dimension; i++) + { + values[i] = float.MaxValue; + values[i + dimension] = 0; + values[i + offset] = float.MinValue; + } + for (int word = 0; word < src.Count; word++) + { + if (_currentVocab.GetWordVector(ref src.Values[word], wordVector)) + { + deno++; + for (int i = 0; i < dimension; i++) + { + float currentTerm = wordVector[i]; + if (values[i] > currentTerm) + values[i] = currentTerm; + values[dimension + i] += currentTerm; + if (values[offset + i] < currentTerm) + values[offset + i] = currentTerm; + } + } + } + + if (deno != 0) + for (int index = 0; index < dimension; index++) + values[index + dimension] /= deno; + + dst = new VBuffer(values.Length, values, dst.Indices); + }; + } + + public enum PretrainedModelKind + { + [TGUI(Label = "GloVe 50D")] + GloVe50D = 0, + + [TGUI(Label = "GloVe 100D")] + GloVe100D = 1, + + [TGUI(Label = "GloVe 200D")] + GloVe200D = 2, + + [TGUI(Label = "GloVe 300D")] + GloVe300D = 3, + + [TGUI(Label = "GloVe Twitter 25D")] + GloVeTwitter25D = 4, + + [TGUI(Label = "GloVe Twitter 50D")] + GloVeTwitter50D = 5, + + [TGUI(Label = "GloVe Twitter 100D")] + GloVeTwitter100D = 6, + + [TGUI(Label = "GloVe Twitter 200D")] + GloVeTwitter200D = 7, + + [TGUI(Label = "fastText Wikipedia 300D")] + FastTextWikipedia300D = 8, + + [TGUI(Label = "Sentiment-Specific Word Embedding")] + Sswe = 9 + } + + private static Dictionary _modelsMetaData = new Dictionary() + { + { PretrainedModelKind.GloVe50D, "glove.6B.50d.txt" }, + { PretrainedModelKind.GloVe100D, "glove.6B.100d.txt" }, + { PretrainedModelKind.GloVe200D, "glove.6B.200d.txt" }, + { PretrainedModelKind.GloVe300D, "glove.6B.300d.txt" }, + { PretrainedModelKind.GloVeTwitter25D, "glove.twitter.27B.25d.txt" }, + { PretrainedModelKind.GloVeTwitter50D, "glove.twitter.27B.50d.txt" }, + { PretrainedModelKind.GloVeTwitter100D, "glove.twitter.27B.100d.txt" }, + { PretrainedModelKind.GloVeTwitter200D, "glove.twitter.27B.200d.txt" }, + { PretrainedModelKind.FastTextWikipedia300D, "wiki.en.vec" }, + { PretrainedModelKind.Sswe, "sentiment.emd" } + }; + + private static Dictionary _linesToSkipInModels = new Dictionary() + { { PretrainedModelKind.FastTextWikipedia300D, 1 } }; + + private string EnsureModelFile(IHostEnvironment env, out int linesToSkip, PretrainedModelKind kind) + { + linesToSkip = 0; + if (_modelsMetaData.ContainsKey(kind)) + { + var modelFileName = _modelsMetaData[kind]; + if (_linesToSkipInModels.ContainsKey(kind)) + linesToSkip = _linesToSkipInModels[kind]; + using (var ch = Host.Start("Ensuring resources")) + { + string dir = kind == PretrainedModelKind.Sswe ? Path.Combine("Text", "Sswe") : "WordVectors"; + var url = $"{dir}/{modelFileName}"; + var ensureModel = ResourceManagerUtils.Instance.EnsureResource(Host, ch, url, modelFileName, dir, Timeout); + ensureModel.Wait(); + var errorResult = ResourceManagerUtils.GetErrorMessage(out var errorMessage, ensureModel.Result); + if (errorResult != null) + { + var directory = Path.GetDirectoryName(errorResult.FileName); + var name = Path.GetFileName(errorResult.FileName); + throw ch.Except($"{errorMessage}\nModel file for Word Embedding transform could not be found! " + + $@"Please copy the model file '{name}' from '{url}' to '{directory}'."); + } + return ensureModel.Result.FileName; + } + } + throw Host.Except($"Can't map model kind = {kind} to specific file, please refer to https://aka.ms/MLNetIssue for assistance"); + } + + private Model GetVocabularyDictionary() + { + int dimension = 0; + if (!File.Exists(_modelFileNameWithPath)) + throw Host.Except("Custom word embedding model file '{0}' could not be found for Word Embeddings transform.", _modelFileNameWithPath); + + if (_vocab.ContainsKey(_modelFileNameWithPath) && _vocab[_modelFileNameWithPath] != null) + { + if (_vocab[_modelFileNameWithPath].TryGetTarget(out Model model)) + { + dimension = model.Dimension; + return model; + } + } + + lock (_embeddingsLock) + { + if (_vocab.ContainsKey(_modelFileNameWithPath) && _vocab[_modelFileNameWithPath] != null) + { + if (_vocab[_modelFileNameWithPath].TryGetTarget(out Model modelObject)) + { + dimension = modelObject.Dimension; + return modelObject; + } + } + + Model model = null; + using (StreamReader sr = File.OpenText(_modelFileNameWithPath)) + { + string line; + int lineNumber = 1; + char[] delimiters = { ' ', '\t' }; + using (var ch = Host.Start(LoaderSignature)) + using (var pch = Host.StartProgressChannel("Building Vocabulary from Model File for Word Embeddings Transform")) + { + var header = new ProgressHeader(new[] { "lines" }); + pch.SetHeader(header, e => e.SetProgress(0, lineNumber)); + string firstLine = sr.ReadLine(); + while ((line = sr.ReadLine()) != null) + { + if (lineNumber >= _linesToSkip) + { + string[] words = line.TrimEnd().Split(delimiters); + dimension = words.Length - 1; + if (model == null) + model = new Model(dimension); + if (model.Dimension != dimension) + ch.Warning($"Dimension mismatch while reading model file: '{_modelFileNameWithPath}', line number {lineNumber + 1}, expected dimension = {model.Dimension}, received dimension = {dimension}"); + else + { + float tmp; + string key = words[0]; + float[] value = words.Skip(1).Select(x => float.TryParse(x, out tmp) ? tmp : Single.NaN).ToArray(); + if (!value.Contains(Single.NaN)) + model.AddWordVector(ch, key, value); + else + ch.Warning($"Parsing error while reading model file: '{_modelFileNameWithPath}', line number {lineNumber + 1}"); + } + } + lineNumber++; + } + + // Handle first line of the embedding file separately since some embedding files including fastText have a single-line header + string[] wordsInFirstLine = firstLine.TrimEnd().Split(delimiters); + dimension = wordsInFirstLine.Length - 1; + if (model == null) + model = new Model(dimension); + float temp; + string firstKey = wordsInFirstLine[0]; + float[] firstValue = wordsInFirstLine.Skip(1).Select(x => float.TryParse(x, out temp) ? temp : Single.NaN).ToArray(); + if (!firstValue.Contains(Single.NaN)) + model.AddWordVector(ch, firstKey, firstValue); + else + ch.Warning($"Parsing error while reading model file: '{_modelFileNameWithPath}', line number 1"); + pch.Checkpoint(lineNumber); + } + } + _vocab[_modelFileNameWithPath] = new WeakReference(model, false); + return model; + } + } + } +} diff --git a/src/Microsoft.ML.Transforms/Text/doc.xml b/src/Microsoft.ML.Transforms/Text/doc.xml index 5f734e1cfd..7f32268899 100644 --- a/src/Microsoft.ML.Transforms/Text/doc.xml +++ b/src/Microsoft.ML.Transforms/Text/doc.xml @@ -74,7 +74,7 @@ The n-grams are represented as count vectors, with vector slots corresponding to n-grams. Embedding ngrams in a vector space allows their contents to be compared in an efficient manner. The slot values in the vector can be weighted by the following factors: - + term frequency the number of occurrences of the slot in the text @@ -169,7 +169,7 @@ For more details please see original LightLDA paper, and its open source implementation. - + LightLDA: Big Topic Models on Modest Computer Clusters LightLDA @@ -179,7 +179,56 @@ - pipeline.Add(new LightLda(("InTextCol" , "OutTextCol"))); + pipeline.Add(new LightLda(("InTextCol" , "OutTextCol"))); + + + + + + + Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence vectors using a pre-trained model. + + + WordEmbeddings wrap different embedding models, such as GloVe. Users can specify which embedding to use. + The available options are various versions of GloVe Models, fastText, and SSWE. + + Note: As WordEmbedding requires a column with text vector, e.g. 'this', 'is', 'good', users need to create an input column by + using the output_tokens=True for TextTransform to convert a column with sentences like 'This is good' into 'this', 'is', 'good'. + The suffix of '_TransformedText' is added to the original column name to create the output token column. For instance if the input column is 'body', + the output tokens column is named 'body_TransformedText'. + + License attributes for pretrained models: + + + + "fastText Wikipedia 300D" by Facebook, Inc. is licensed under CC-BY-SA 3.0 based on: + P. Bojanowski*, E. Grave*, A. Joulin, T. Mikolov,Enriching Word Vectors with Subword Information + More information can be found here. + + + + + GloVe models by Stanford University, or (Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. + GloVe: Global Vectors for Word Representation) is licensed under PDDL. + More information can be found here. + Repository can be found here. + + + + + + + + + + pipeline.Add(new TextFeaturizer("Words", "InputTextCol") + { + TextCase = TextNormalizerTransformCaseNormalizationMode.Lower, + OutputTokens = true, + CharFeatureExtractor=null, + WordFeatureExtractor = null + }); + pipeline.Add(new WordEmbeddings(("Words_TransformedText", "OutTextCol"))); diff --git a/src/Microsoft.ML.Transforms/WhiteningTransform.cs b/src/Microsoft.ML.Transforms/WhiteningTransform.cs index 6854157f31..5d1b3188b7 100644 --- a/src/Microsoft.ML.Transforms/WhiteningTransform.cs +++ b/src/Microsoft.ML.Transforms/WhiteningTransform.cs @@ -591,13 +591,13 @@ private static void FillValues(Float[] model, ref VBuffer src, ref VBuffe private static Float DotProduct(Float[] a, int aOffset, Float[] b, int[] indices, int count) { Contracts.Assert(count <= indices.Length); - return SseUtils.DotProductSparse(a, aOffset, b, indices, count); + return CpuMathUtils.DotProductSparse(a, aOffset, b, indices, count); } private static class Mkl { - private const string DllName = "Microsoft.ML.MklImports.dll"; + private const string DllName = "MklImports"; public enum Layout { diff --git a/src/Microsoft.ML.Transforms/doc.xml b/src/Microsoft.ML.Transforms/doc.xml index 63d2765afc..af8123f339 100644 --- a/src/Microsoft.ML.Transforms/doc.xml +++ b/src/Microsoft.ML.Transforms/doc.xml @@ -29,10 +29,11 @@ Converts the categorical value into an indicator array by building a dictionary of categories based on the data and using the id in the dictionary as the index in the array - The CategoricalOneHotVectorizer transform passes through a data set, operating on text columns, to + + The CategoricalOneHotVectorizer transform passes through a data set, operating on text columns, to build a dictionary of categories. - For each row, the entire text string appearing in the input column is defined as a category. - The output of this transform is an indicator vector. + For each row, the entire text string appearing in the input column is defined as a category. + The output of this transform is an indicator vector. Each slot in this vector corresponds to a category in the dictionary, so its length is the size of the built dictionary. The CategoricalOneHotVectorizer can be applied to one or more columns, in which case it builds and uses a separate dictionary for each column that it is applied to. @@ -64,7 +65,7 @@ This transform uses a set of aggregators to count the number of non-default values for each slot and instantiates a to actually drop the slots. - This transform is useful when applied together with a . + This transform is useful when applied together with a . The count feature selection can remove those features generated by the hash transform that have no data in the examples. @@ -171,7 +172,7 @@ This transform can transform either scalars or vectors (both fixed and variable size), creating output columns that indicate, through the true/false booleans whether the row has a missing value. - + @@ -192,14 +193,14 @@ with either the default value, user input, or imputed values (min/max/mean are currently supported). Imputation modes are supported for vectors both by slot and across all slots. - + pipeline.Add(new MissingValueSubstitutor("FeatureCol") { - ReplacementKind = NAReplaceTransformReplacementKind.Mean + ReplacementKind = NAReplaceTransformReplacementKind.Mean }); @@ -221,7 +222,7 @@ pipeline.Add(new LpNormalizer("FeatureCol") { - NormKind = LpNormNormalizerTransformNormalizerKind.L1Norm + NormKind = LpNormNormalizerTransformNormalizerKind.L1Norm }); @@ -235,14 +236,15 @@ Scaling inputs to unit norms is a common operation for text classification or clustering. - For more information see: + For more information see: + An Analysis of Single-Layer Networks in Unsupervised Feature Learning pipeline.Add(new GlobalContrastNormalizer("FeatureCol") { - SubMean= false + SubMean= false }); @@ -334,7 +336,7 @@ pipeline.Add(new CombinerByContiguousGroupId { - GroupKey = new []{"Key1", "Key2" } + GroupKey = new []{"Key1", "Key2" } }); diff --git a/src/Microsoft.ML/CSharpApi.cs b/src/Microsoft.ML/CSharpApi.cs index 3ac9a2acfb..2bc29fda47 100644 --- a/src/Microsoft.ML/CSharpApi.cs +++ b/src/Microsoft.ML/CSharpApi.cs @@ -754,6 +754,18 @@ public void Add(Microsoft.ML.Trainers.OnlineGradientDescentRegressor input, Micr _jsonNodes.Add(Serialize("Trainers.OnlineGradientDescentRegressor", input, output)); } + public Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input) + { + var output = new Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input, Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output output) + { + _jsonNodes.Add(Serialize("Trainers.OrdinaryLeastSquaresRegressor", input, output)); + } + public Microsoft.ML.Trainers.PcaAnomalyDetector.Output Add(Microsoft.ML.Trainers.PcaAnomalyDetector input) { var output = new Microsoft.ML.Trainers.PcaAnomalyDetector.Output(); @@ -826,6 +838,18 @@ public void Add(Microsoft.ML.Trainers.StochasticGradientDescentBinaryClassifier _jsonNodes.Add(Serialize("Trainers.StochasticGradientDescentBinaryClassifier", input, output)); } + public Microsoft.ML.Trainers.SymSgdBinaryClassifier.Output Add(Microsoft.ML.Trainers.SymSgdBinaryClassifier input) + { + var output = new Microsoft.ML.Trainers.SymSgdBinaryClassifier.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.SymSgdBinaryClassifier input, Microsoft.ML.Trainers.SymSgdBinaryClassifier.Output output) + { + _jsonNodes.Add(Serialize("Trainers.SymSgdBinaryClassifier", input, output)); + } + public Microsoft.ML.Transforms.ApproximateBootstrapSampler.Output Add(Microsoft.ML.Transforms.ApproximateBootstrapSampler input) { var output = new Microsoft.ML.Transforms.ApproximateBootstrapSampler.Output(); @@ -1090,6 +1114,54 @@ public void Add(Microsoft.ML.Transforms.HashConverter input, Microsoft.ML.Transf _jsonNodes.Add(Serialize("Transforms.HashConverter", input, output)); } + public Microsoft.ML.Transforms.ImageGrayscale.Output Add(Microsoft.ML.Transforms.ImageGrayscale input) + { + var output = new Microsoft.ML.Transforms.ImageGrayscale.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.ImageGrayscale input, Microsoft.ML.Transforms.ImageGrayscale.Output output) + { + _jsonNodes.Add(Serialize("Transforms.ImageGrayscale", input, output)); + } + + public Microsoft.ML.Transforms.ImageLoader.Output Add(Microsoft.ML.Transforms.ImageLoader input) + { + var output = new Microsoft.ML.Transforms.ImageLoader.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.ImageLoader input, Microsoft.ML.Transforms.ImageLoader.Output output) + { + _jsonNodes.Add(Serialize("Transforms.ImageLoader", input, output)); + } + + public Microsoft.ML.Transforms.ImagePixelExtractor.Output Add(Microsoft.ML.Transforms.ImagePixelExtractor input) + { + var output = new Microsoft.ML.Transforms.ImagePixelExtractor.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.ImagePixelExtractor input, Microsoft.ML.Transforms.ImagePixelExtractor.Output output) + { + _jsonNodes.Add(Serialize("Transforms.ImagePixelExtractor", input, output)); + } + + public Microsoft.ML.Transforms.ImageResizer.Output Add(Microsoft.ML.Transforms.ImageResizer input) + { + var output = new Microsoft.ML.Transforms.ImageResizer.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.ImageResizer input, Microsoft.ML.Transforms.ImageResizer.Output output) + { + _jsonNodes.Add(Serialize("Transforms.ImageResizer", input, output)); + } + public Microsoft.ML.Transforms.KeyToTextConverter.Output Add(Microsoft.ML.Transforms.KeyToTextConverter input) { var output = new Microsoft.ML.Transforms.KeyToTextConverter.Output(); @@ -1522,6 +1594,30 @@ public void Add(Microsoft.ML.Transforms.TwoHeterogeneousModelCombiner input, Mic _jsonNodes.Add(Serialize("Transforms.TwoHeterogeneousModelCombiner", input, output)); } + public Microsoft.ML.Transforms.VectorToImage.Output Add(Microsoft.ML.Transforms.VectorToImage input) + { + var output = new Microsoft.ML.Transforms.VectorToImage.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.VectorToImage input, Microsoft.ML.Transforms.VectorToImage.Output output) + { + _jsonNodes.Add(Serialize("Transforms.VectorToImage", input, output)); + } + + public Microsoft.ML.Transforms.WordEmbeddings.Output Add(Microsoft.ML.Transforms.WordEmbeddings input) + { + var output = new Microsoft.ML.Transforms.WordEmbeddings.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.WordEmbeddings input, Microsoft.ML.Transforms.WordEmbeddings.Output output) + { + _jsonNodes.Add(Serialize("Transforms.WordEmbeddings", input, output)); + } + public Microsoft.ML.Transforms.WordTokenizer.Output Add(Microsoft.ML.Transforms.WordTokenizer input) { var output = new Microsoft.ML.Transforms.WordTokenizer.Output(); @@ -8824,6 +8920,93 @@ public OnlineGradientDescentRegressorPipelineStep(Output output) } } + namespace Trainers + { + + /// + public sealed partial class OrdinaryLeastSquaresRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// L2 regularization weight + /// + [TlcModule.SweepableDiscreteParamAttribute("L2Weight", new object[]{1E-06f, 0.1f, 1f})] + public float L2Weight { get; set; } = 1E-06f; + + /// + /// Whether to calculate per parameter significance statistics + /// + public bool PerParameterSignificance { get; set; } = true; + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// The data to be used for training + /// + public Var TrainingData { get; set; } = new Var(); + + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; + + /// + /// Normalize option for the feature column + /// + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(OrdinaryLeastSquaresRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new OrdinaryLeastSquaresRegressorPipelineStep(output); + } + + private class OrdinaryLeastSquaresRegressorPipelineStep : ILearningPipelinePredictorStep + { + public OrdinaryLeastSquaresRegressorPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + namespace Trainers { @@ -9590,6 +9773,126 @@ public StochasticGradientDescentBinaryClassifierPipelineStep(Output output) } } + namespace Trainers + { + + /// + public sealed partial class SymSgdBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// Degree of lock-free parallelism. Determinism not guaranteed. Multi-threading is not supported currently. + /// + public int? NumberOfThreads { get; set; } + + /// + /// Number of passes over the data. + /// + [TlcModule.SweepableDiscreteParamAttribute("NumberOfIterations", new object[]{1, 5, 10, 20, 30, 40, 50})] + public int NumberOfIterations { get; set; } = 50; + + /// + /// Tolerance for difference in average loss in consecutive passes. + /// + public float Tolerance { get; set; } = 0.0001f; + + /// + /// Learning rate + /// + [TlcModule.SweepableDiscreteParamAttribute("LearningRate", new object[]{"", 10f, 1f, 0.1f, 0.01f, 0.001f})] + public float? LearningRate { get; set; } + + /// + /// L2 regularization + /// + [TlcModule.SweepableDiscreteParamAttribute("L2Regularization", new object[]{0f, 1E-05f, 1E-05f, 1E-06f, 1E-07f})] + public float L2Regularization { get; set; } + + /// + /// The number of iterations each thread learns a local model until combining it with the global model. Low value means more updated global model and high value means less cache traffic. + /// + [TlcModule.SweepableDiscreteParamAttribute("UpdateFrequency", new object[]{"", 5, 20})] + public int? UpdateFrequency { get; set; } + + /// + /// The acceleration memory budget in MB + /// + public long MemorySize { get; set; } = 1024; + + /// + /// Shuffle data? + /// + public bool Shuffle { get; set; } = true; + + /// + /// Apply weight to the positive class, for imbalanced data + /// + public float PositiveInstanceWeight { get; set; } = 1f; + + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// The data to be used for training + /// + public Var TrainingData { get; set; } = new Var(); + + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; + + /// + /// Normalize option for the feature column + /// + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(SymSgdBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new SymSgdBinaryClassifierPipelineStep(output); + } + + private class SymSgdBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + { + public SymSgdBinaryClassifierPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + namespace Transforms { @@ -11875,7 +12178,7 @@ public HashConverterPipelineStep(Output output) namespace Transforms { - public sealed partial class KeyToValueTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class ImageGrayscaleTransformColumn : OneToOneColumn, IOneToOneColumn { /// /// Name of the new column @@ -11889,15 +12192,17 @@ public sealed partial class KeyToValueTransformColumn : OneToOneColumn - public sealed partial class KeyToTextConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Convert image into grayscale. + /// + public sealed partial class ImageGrayscale : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public KeyToTextConverter() + public ImageGrayscale() { } - public KeyToTextConverter(params string[] inputColumns) + public ImageGrayscale(params string[] inputColumns) { if (inputColumns != null) { @@ -11908,7 +12213,7 @@ public KeyToTextConverter(params string[] inputColumns) } } - public KeyToTextConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public ImageGrayscale(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -11921,15 +12226,15 @@ public KeyToTextConverter(params (string inputColumn, string outputColumn)[] inp public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -11937,7 +12242,7 @@ public void AddColumn(string outputColumn, string inputColumn) /// /// New column definition(s) (optional form: name:src) /// - public KeyToValueTransformColumn[] Column { get; set; } + public ImageGrayscaleTransformColumn[] Column { get; set; } /// /// Input dataset @@ -11966,18 +12271,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(KeyToTextConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ImageGrayscale)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new KeyToTextConverterPipelineStep(output); + return new ImageGrayscalePipelineStep(output); } - private class KeyToTextConverterPipelineStep : ILearningPipelineDataStep + private class ImageGrayscalePipelineStep : ILearningPipelineDataStep { - public KeyToTextConverterPipelineStep(Output output) + public ImageGrayscalePipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11992,22 +12297,76 @@ public KeyToTextConverterPipelineStep(Output output) namespace Transforms { - /// - /// Transforms the label to either key or bool (if needed) to make it suitable for classification. + public sealed partial class ImageLoaderTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// Load images from files. /// - public sealed partial class LabelColumnKeyBooleanConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ImageLoader : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { + public ImageLoader() + { + } + + public ImageLoader(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public ImageLoader(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + /// - /// Convert the key values to text + /// New column definition(s) (optional form: name:src) /// - public bool TextKeyValues { get; set; } = true; + public ImageLoaderTransformColumn[] Column { get; set; } /// - /// The label column + /// Folder where to search for images /// - public string LabelColumn { get; set; } + public string ImageFolder { get; set; } /// /// Input dataset @@ -12036,18 +12395,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LabelColumnKeyBooleanConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ImageLoader)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LabelColumnKeyBooleanConverterPipelineStep(output); + return new ImageLoaderPipelineStep(output); } - private class LabelColumnKeyBooleanConverterPipelineStep : ILearningPipelineDataStep + private class ImageLoaderPipelineStep : ILearningPipelineDataStep { - public LabelColumnKeyBooleanConverterPipelineStep(Output output) + public ImageLoaderPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12062,12 +12421,47 @@ public LabelColumnKeyBooleanConverterPipelineStep(Output output) namespace Transforms { - public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class ImagePixelExtractorTransformColumn : OneToOneColumn, IOneToOneColumn { /// - /// The positive example class for binary classification. + /// Whether to use alpha channel /// - public int? ClassIndex { get; set; } + public bool? UseAlpha { get; set; } + + /// + /// Whether to use red channel + /// + public bool? UseRed { get; set; } + + /// + /// Whether to use green channel + /// + public bool? UseGreen { get; set; } + + /// + /// Whether to use blue channel + /// + public bool? UseBlue { get; set; } + + /// + /// Whether to separate each channel or interleave in ARGB order + /// + public bool? InterleaveArgb { get; set; } + + /// + /// Whether to convert to floating point + /// + public bool? Convert { get; set; } + + /// + /// Offset (pre-scale) + /// + public float? Offset { get; set; } + + /// + /// Scale factor + /// + public float? Scale { get; set; } /// /// Name of the new column @@ -12082,16 +12476,16 @@ public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn - public sealed partial class LabelIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ImagePixelExtractor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LabelIndicator() + public ImagePixelExtractor() { } - public LabelIndicator(params string[] inputColumns) + public ImagePixelExtractor(params string[] inputColumns) { if (inputColumns != null) { @@ -12102,7 +12496,7 @@ public LabelIndicator(params string[] inputColumns) } } - public LabelIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public ImagePixelExtractor(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -12115,15 +12509,15 @@ public LabelIndicator(params (string inputColumn, string outputColumn)[] inputOu public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -12131,77 +12525,47 @@ public void AddColumn(string outputColumn, string inputColumn) /// /// New column definition(s) (optional form: name:src) /// - public LabelIndicatorTransformColumn[] Column { get; set; } + public ImagePixelExtractorTransformColumn[] Column { get; set; } /// - /// Label of the positive class. + /// Whether to use alpha channel /// - public int ClassIndex { get; set; } + public bool UseAlpha { get; set; } = false; /// - /// Input dataset + /// Whether to use red channel /// - public Var Data { get; set; } = new Var(); + public bool UseRed { get; set; } = true; + /// + /// Whether to use green channel + /// + public bool UseGreen { get; set; } = true; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); - - } - public Var GetInputData() => Data; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(LabelIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - Data = dataStep.Data; - } - Output output = experiment.Add(this); - return new LabelIndicatorPipelineStep(output); - } - - private class LabelIndicatorPipelineStep : ILearningPipelineDataStep - { - public LabelIndicatorPipelineStep(Output output) - { - Data = output.OutputData; - Model = output.Model; - } - - public Var Data { get; } - public Var Model { get; } - } - } - } + /// + /// Whether to use blue channel + /// + public bool UseBlue { get; set; } = true; - namespace Transforms - { + /// + /// Whether to separate each channel or interleave in ARGB order + /// + public bool InterleaveArgb { get; set; } = false; - /// - /// Transforms the label to float to make it suitable for regression. - /// - public sealed partial class LabelToFloatConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// Whether to convert to floating point + /// + public bool Convert { get; set; } = true; + /// + /// Offset (pre-scale) + /// + public float? Offset { get; set; } /// - /// The label column + /// Scale factor /// - public string LabelColumn { get; set; } + public float? Scale { get; set; } /// /// Input dataset @@ -12230,18 +12594,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LabelToFloatConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ImagePixelExtractor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LabelToFloatConverterPipelineStep(output); + return new ImagePixelExtractorPipelineStep(output); } - private class LabelToFloatConverterPipelineStep : ILearningPipelineDataStep + private class ImagePixelExtractorPipelineStep : ILearningPipelineDataStep { - public LabelToFloatConverterPipelineStep(Output output) + public ImagePixelExtractorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12255,63 +12619,43 @@ public LabelToFloatConverterPipelineStep(Output output) namespace Transforms { - - public sealed partial class LdaTransformColumn : OneToOneColumn, IOneToOneColumn + public enum ImageResizerTransformResizingKind : byte { - /// - /// The number of topics in the LDA - /// - public int? NumTopic { get; set; } - - /// - /// Dirichlet prior on document-topic vectors - /// - public float? AlphaSum { get; set; } - - /// - /// Dirichlet prior on vocab-topic vectors - /// - public float? Beta { get; set; } - - /// - /// Number of Metropolis Hasting step - /// - public int? Mhstep { get; set; } - - /// - /// Number of iterations - /// - public int? NumIterations { get; set; } + IsoPad = 0, + IsoCrop = 1 + } - /// - /// Compute log likelihood over local dataset on this iteration interval - /// - public int? LikelihoodInterval { get; set; } + public enum ImageResizerTransformAnchor : byte + { + Right = 0, + Left = 1, + Top = 2, + Bottom = 3, + Center = 4 + } - /// - /// The number of training threads - /// - public int? NumThreads { get; set; } + public sealed partial class ImageResizerTransformColumn : OneToOneColumn, IOneToOneColumn + { /// - /// The threshold of maximum count of tokens per doc + /// Width of the resized image /// - public int? NumMaxDocToken { get; set; } + public int? ImageWidth { get; set; } /// - /// The number of words to summarize the topic + /// Height of the resized image /// - public int? NumSummaryTermPerTopic { get; set; } + public int? ImageHeight { get; set; } /// - /// The number of burn-in iterations + /// Resizing method /// - public int? NumBurninIterations { get; set; } = 10; + public ImageResizerTransformResizingKind? Resizing { get; set; } /// - /// Reset the random number generator for each document + /// Anchor for cropping /// - public bool? ResetRandomGenerator { get; set; } + public ImageResizerTransformAnchor? CropAnchor { get; set; } /// /// Name of the new column @@ -12325,16 +12669,17 @@ public sealed partial class LdaTransformColumn : OneToOneColumn - /// - public sealed partial class LightLda : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Scales an image to specified dimensions using one of the three scale types: isotropic with padding, isotropic with cropping or anisotropic. In case of isotropic padding, transparent color is used to pad resulting image. + /// + public sealed partial class ImageResizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LightLda() + public ImageResizer() { } - public LightLda(params string[] inputColumns) + public ImageResizer(params string[] inputColumns) { if (inputColumns != null) { @@ -12345,7 +12690,7 @@ public LightLda(params string[] inputColumns) } } - public LightLda(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public ImageResizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -12358,102 +12703,56 @@ public LightLda(params (string inputColumn, string outputColumn)[] inputOutputCo public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:srcs) + /// New column definition(s) (optional form: name:src) /// - public LdaTransformColumn[] Column { get; set; } + public ImageResizerTransformColumn[] Column { get; set; } /// - /// The number of topics in the LDA + /// Resized width of the image /// - [TlcModule.SweepableDiscreteParamAttribute("NumTopic", new object[]{20, 40, 100, 200})] - public int NumTopic { get; set; } = 100; + public int ImageWidth { get; set; } /// - /// Dirichlet prior on document-topic vectors + /// Resized height of the image /// - [TlcModule.SweepableDiscreteParamAttribute("AlphaSum", new object[]{1, 10, 100, 200})] - public float AlphaSum { get; set; } = 100f; + public int ImageHeight { get; set; } /// - /// Dirichlet prior on vocab-topic vectors + /// Resizing method /// - [TlcModule.SweepableDiscreteParamAttribute("Beta", new object[]{0.01f, 0.015f, 0.07f, 0.02f})] - public float Beta { get; set; } = 0.01f; + public ImageResizerTransformResizingKind Resizing { get; set; } = ImageResizerTransformResizingKind.IsoCrop; /// - /// Number of Metropolis Hasting step + /// Anchor for cropping /// - [TlcModule.SweepableDiscreteParamAttribute("Mhstep", new object[]{2, 4, 8, 16})] - public int Mhstep { get; set; } = 4; + public ImageResizerTransformAnchor CropAnchor { get; set; } = ImageResizerTransformAnchor.Center; /// - /// Number of iterations + /// Input dataset /// - [TlcModule.SweepableDiscreteParamAttribute("NumIterations", new object[]{100, 200, 300, 400})] - public int NumIterations { get; set; } = 200; + public Var Data { get; set; } = new Var(); - /// - /// Compute log likelihood over local dataset on this iteration interval - /// - public int LikelihoodInterval { get; set; } = 5; - /// - /// The threshold of maximum count of tokens per doc - /// - public int NumMaxDocToken { get; set; } = 512; - - /// - /// The number of training threads. Default value depends on number of logical processors. - /// - public int? NumThreads { get; set; } - - /// - /// The number of words to summarize the topic - /// - public int NumSummaryTermPerTopic { get; set; } = 10; - - /// - /// The number of burn-in iterations - /// - [TlcModule.SweepableDiscreteParamAttribute("NumBurninIterations", new object[]{10, 20, 30, 40})] - public int NumBurninIterations { get; set; } = 10; - - /// - /// Reset the random number generator for each document - /// - public bool ResetRandomGenerator { get; set; } = false; - - /// - /// Whether to output the topic-word summary in text format - /// - public bool OutputTopicWordSummary { get; set; } = false; - - /// - /// Input dataset - /// - public Var Data { get; set; } = new Var(); - - - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); /// /// Transform model @@ -12469,18 +12768,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LightLda)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ImageResizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LightLdaPipelineStep(output); + return new ImageResizerPipelineStep(output); } - private class LightLdaPipelineStep : ILearningPipelineDataStep + private class ImageResizerPipelineStep : ILearningPipelineDataStep { - public LightLdaPipelineStep(Output output) + public ImageResizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12495,13 +12794,8 @@ public LightLdaPipelineStep(Output output) namespace Transforms { - public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class KeyToValueTransformColumn : OneToOneColumn, IOneToOneColumn { - /// - /// Max number of examples used to train the normalizer - /// - public long? MaxTrainingExamples { get; set; } - /// /// Name of the new column /// @@ -12514,17 +12808,15 @@ public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn - /// Normalizes the data based on the computed mean and variance of the logarithm of the data. - /// - public sealed partial class LogMeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + public sealed partial class KeyToTextConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LogMeanVarianceNormalizer() + public KeyToTextConverter() { } - public LogMeanVarianceNormalizer(params string[] inputColumns) + public KeyToTextConverter(params string[] inputColumns) { if (inputColumns != null) { @@ -12535,7 +12827,7 @@ public LogMeanVarianceNormalizer(params string[] inputColumns) } } - public LogMeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public KeyToTextConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -12548,33 +12840,23 @@ public LogMeanVarianceNormalizer(params (string inputColumn, string outputColumn public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } - /// - /// Whether to use CDF as the output - /// - public bool UseCdf { get; set; } = true; - /// /// New column definition(s) (optional form: name:src) /// - public NormalizeTransformLogNormalColumn[] Column { get; set; } - - /// - /// Max number of examples used to train the normalizer - /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public KeyToValueTransformColumn[] Column { get; set; } /// /// Input dataset @@ -12603,18 +12885,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LogMeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(KeyToTextConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LogMeanVarianceNormalizerPipelineStep(output); + return new KeyToTextConverterPipelineStep(output); } - private class LogMeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep + private class KeyToTextConverterPipelineStep : ILearningPipelineDataStep { - public LogMeanVarianceNormalizerPipelineStep(Output output) + public KeyToTextConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12628,98 +12910,23 @@ public LogMeanVarianceNormalizerPipelineStep(Output output) namespace Transforms { - public enum LpNormNormalizerTransformNormalizerKind : byte - { - L2Norm = 0, - StdDev = 1, - L1Norm = 2, - LInf = 3 - } - - public sealed partial class LpNormNormalizerTransformColumn : OneToOneColumn, IOneToOneColumn - { - /// - /// The norm to use to normalize each sample - /// - public LpNormNormalizerTransformNormalizerKind? NormKind { get; set; } - - /// - /// Subtract mean from each value before normalizing - /// - public bool? SubMean { get; set; } - - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } - - } - - /// - public sealed partial class LpNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Transforms the label to either key or bool (if needed) to make it suitable for classification. + /// + public sealed partial class LabelColumnKeyBooleanConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LpNormalizer() - { - } - - public LpNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public LpNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (var inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); - } - } - } - - public void AddColumn(string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); - Column = list.ToArray(); - } - - public void AddColumn(string outputColumn, string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); - Column = list.ToArray(); - } - - - /// - /// New column definition(s) (optional form: name:src) - /// - public LpNormNormalizerTransformColumn[] Column { get; set; } /// - /// The norm to use to normalize each sample + /// Convert the key values to text /// - public LpNormNormalizerTransformNormalizerKind NormKind { get; set; } = LpNormNormalizerTransformNormalizerKind.L2Norm; + public bool TextKeyValues { get; set; } = true; /// - /// Subtract mean from each value before normalizing + /// The label column /// - public bool SubMean { get; set; } = false; + public string LabelColumn { get; set; } /// /// Input dataset @@ -12748,18 +12955,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LpNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LabelColumnKeyBooleanConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LpNormalizerPipelineStep(output); + return new LabelColumnKeyBooleanConverterPipelineStep(output); } - private class LpNormalizerPipelineStep : ILearningPipelineDataStep + private class LabelColumnKeyBooleanConverterPipelineStep : ILearningPipelineDataStep { - public LpNormalizerPipelineStep(Output output) + public LabelColumnKeyBooleanConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12774,49 +12981,36 @@ public LpNormalizerPipelineStep(Output output) namespace Transforms { - /// - /// Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel. - /// - public sealed partial class ManyHeterogeneousModelCombiner + public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn { - - /// - /// Transform model + /// The positive example class for binary classification. /// - public ArrayVar TransformModels { get; set; } = new ArrayVar(); + public int? ClassIndex { get; set; } /// - /// Predictor model + /// Name of the new column /// - public Var PredictorModel { get; set; } = new Var(); - + public string Name { get; set; } - public sealed class Output - { - /// - /// Predictor model - /// - public Var PredictorModel { get; set; } = new Var(); + /// + /// Name of the source column + /// + public string Source { get; set; } - } } - } - - namespace Transforms - { /// - /// Normalizes the data based on the computed mean and variance of the data. + /// Label remapper used by OVA /// - public sealed partial class MeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LabelIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MeanVarianceNormalizer() + public LabelIndicator() { } - public MeanVarianceNormalizer(params string[] inputColumns) + public LabelIndicator(params string[] inputColumns) { if (inputColumns != null) { @@ -12827,7 +13021,7 @@ public MeanVarianceNormalizer(params string[] inputColumns) } } - public MeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public LabelIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -12840,38 +13034,28 @@ public MeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } - /// - /// Whether to use CDF as the output - /// - public bool UseCdf { get; set; } = false; - /// /// New column definition(s) (optional form: name:src) /// - public NormalizeTransformAffineColumn[] Column { get; set; } - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; + public LabelIndicatorTransformColumn[] Column { get; set; } /// - /// Max number of examples used to train the normalizer + /// Label of the positive class. /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public int ClassIndex { get; set; } /// /// Input dataset @@ -12900,18 +13084,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LabelIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MeanVarianceNormalizerPipelineStep(output); + return new LabelIndicatorPipelineStep(output); } - private class MeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep + private class LabelIndicatorPipelineStep : ILearningPipelineDataStep { - public MeanVarianceNormalizerPipelineStep(Output output) + public LabelIndicatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12927,66 +13111,16 @@ namespace Transforms { /// - /// Normalizes the data based on the observed minimum and maximum values of the data. + /// Transforms the label to float to make it suitable for regression. /// - public sealed partial class MinMaxNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LabelToFloatConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MinMaxNormalizer() - { - } - - public MinMaxNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public MinMaxNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (var inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); - } - } - } - - public void AddColumn(string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); - Column = list.ToArray(); - } - - public void AddColumn(string outputColumn, string inputColumn) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); - Column = list.ToArray(); - } - - - /// - /// New column definition(s) (optional form: name:src) - /// - public NormalizeTransformAffineColumn[] Column { get; set; } - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; /// - /// Max number of examples used to train the normalizer + /// The label column /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public string LabelColumn { get; set; } /// /// Input dataset @@ -13015,18 +13149,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MinMaxNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LabelToFloatConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MinMaxNormalizerPipelineStep(output); + return new LabelToFloatConverterPipelineStep(output); } - private class MinMaxNormalizerPipelineStep : ILearningPipelineDataStep + private class LabelToFloatConverterPipelineStep : ILearningPipelineDataStep { - public MinMaxNormalizerPipelineStep(Output output) + public LabelToFloatConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13040,31 +13174,63 @@ public MinMaxNormalizerPipelineStep(Output output) namespace Transforms { - public enum NAHandleTransformReplacementKind + + public sealed partial class LdaTransformColumn : OneToOneColumn, IOneToOneColumn { - DefaultValue = 0, - Mean = 1, - Minimum = 2, - Maximum = 3 - } + /// + /// The number of topics in the LDA + /// + public int? NumTopic { get; set; } + /// + /// Dirichlet prior on document-topic vectors + /// + public float? AlphaSum { get; set; } - public sealed partial class NAHandleTransformColumn : OneToOneColumn, IOneToOneColumn - { /// - /// The replacement method to utilize + /// Dirichlet prior on vocab-topic vectors /// - public NAHandleTransformReplacementKind? Kind { get; set; } + public float? Beta { get; set; } /// - /// Whether to impute values by slot + /// Number of Metropolis Hasting step /// - public bool? ImputeBySlot { get; set; } + public int? Mhstep { get; set; } /// - /// Whether or not to concatenate an indicator vector column to the value column + /// Number of iterations /// - public bool? ConcatIndicator { get; set; } + public int? NumIterations { get; set; } + + /// + /// Compute log likelihood over local dataset on this iteration interval + /// + public int? LikelihoodInterval { get; set; } + + /// + /// The number of training threads + /// + public int? NumThreads { get; set; } + + /// + /// The threshold of maximum count of tokens per doc + /// + public int? NumMaxDocToken { get; set; } + + /// + /// The number of words to summarize the topic + /// + public int? NumSummaryTermPerTopic { get; set; } + + /// + /// The number of burn-in iterations + /// + public int? NumBurninIterations { get; set; } = 10; + + /// + /// Reset the random number generator for each document + /// + public bool? ResetRandomGenerator { get; set; } /// /// Name of the new column @@ -13078,16 +13244,16 @@ public sealed partial class NAHandleTransformColumn : OneToOneColumn - /// - public sealed partial class MissingValueHandler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// + public sealed partial class LightLda : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueHandler() + public LightLda() { } - public MissingValueHandler(params string[] inputColumns) + public LightLda(params string[] inputColumns) { if (inputColumns != null) { @@ -13098,7 +13264,7 @@ public MissingValueHandler(params string[] inputColumns) } } - public MissingValueHandler(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public LightLda(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -13111,38 +13277,89 @@ public MissingValueHandler(params (string inputColumn, string outputColumn)[] in public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:rep:src) + /// New column definition(s) (optional form: name:srcs) /// - public NAHandleTransformColumn[] Column { get; set; } + public LdaTransformColumn[] Column { get; set; } /// - /// The replacement method to utilize + /// The number of topics in the LDA /// - public NAHandleTransformReplacementKind ReplaceWith { get; set; } = NAHandleTransformReplacementKind.DefaultValue; + [TlcModule.SweepableDiscreteParamAttribute("NumTopic", new object[]{20, 40, 100, 200})] + public int NumTopic { get; set; } = 100; /// - /// Whether to impute values by slot + /// Dirichlet prior on document-topic vectors /// - public bool ImputeBySlot { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("AlphaSum", new object[]{1, 10, 100, 200})] + public float AlphaSum { get; set; } = 100f; /// - /// Whether or not to concatenate an indicator vector column to the value column + /// Dirichlet prior on vocab-topic vectors /// - public bool Concat { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("Beta", new object[]{0.01f, 0.015f, 0.07f, 0.02f})] + public float Beta { get; set; } = 0.01f; + + /// + /// Number of Metropolis Hasting step + /// + [TlcModule.SweepableDiscreteParamAttribute("Mhstep", new object[]{2, 4, 8, 16})] + public int Mhstep { get; set; } = 4; + + /// + /// Number of iterations + /// + [TlcModule.SweepableDiscreteParamAttribute("NumIterations", new object[]{100, 200, 300, 400})] + public int NumIterations { get; set; } = 200; + + /// + /// Compute log likelihood over local dataset on this iteration interval + /// + public int LikelihoodInterval { get; set; } = 5; + + /// + /// The threshold of maximum count of tokens per doc + /// + public int NumMaxDocToken { get; set; } = 512; + + /// + /// The number of training threads. Default value depends on number of logical processors. + /// + public int? NumThreads { get; set; } + + /// + /// The number of words to summarize the topic + /// + public int NumSummaryTermPerTopic { get; set; } = 10; + + /// + /// The number of burn-in iterations + /// + [TlcModule.SweepableDiscreteParamAttribute("NumBurninIterations", new object[]{10, 20, 30, 40})] + public int NumBurninIterations { get; set; } = 10; + + /// + /// Reset the random number generator for each document + /// + public bool ResetRandomGenerator { get; set; } = false; + + /// + /// Whether to output the topic-word summary in text format + /// + public bool OutputTopicWordSummary { get; set; } = false; /// /// Input dataset @@ -13171,18 +13388,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueHandler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightLda)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueHandlerPipelineStep(output); + return new LightLdaPipelineStep(output); } - private class MissingValueHandlerPipelineStep : ILearningPipelineDataStep + private class LightLdaPipelineStep : ILearningPipelineDataStep { - public MissingValueHandlerPipelineStep(Output output) + public LightLdaPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13197,8 +13414,13 @@ public MissingValueHandlerPipelineStep(Output output) namespace Transforms { - public sealed partial class NAIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn, IOneToOneColumn { + /// + /// Max number of examples used to train the normalizer + /// + public long? MaxTrainingExamples { get; set; } + /// /// Name of the new column /// @@ -13211,16 +13433,17 @@ public sealed partial class NAIndicatorTransformColumn : OneToOneColumn - /// - public sealed partial class MissingValueIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Normalizes the data based on the computed mean and variance of the logarithm of the data. + /// + public sealed partial class LogMeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueIndicator() + public LogMeanVarianceNormalizer() { } - public MissingValueIndicator(params string[] inputColumns) + public LogMeanVarianceNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -13231,7 +13454,7 @@ public MissingValueIndicator(params string[] inputColumns) } } - public MissingValueIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public LogMeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -13244,23 +13467,33 @@ public MissingValueIndicator(params (string inputColumn, string outputColumn)[] public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:src) + /// Whether to use CDF as the output /// - public NAIndicatorTransformColumn[] Column { get; set; } + public bool UseCdf { get; set; } = true; + + /// + /// New column definition(s) (optional form: name:src) + /// + public NormalizeTransformLogNormalColumn[] Column { get; set; } + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -13289,18 +13522,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LogMeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueIndicatorPipelineStep(output); + return new LogMeanVarianceNormalizerPipelineStep(output); } - private class MissingValueIndicatorPipelineStep : ILearningPipelineDataStep + private class LogMeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValueIndicatorPipelineStep(Output output) + public LogMeanVarianceNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13314,9 +13547,27 @@ public MissingValueIndicatorPipelineStep(Output output) namespace Transforms { + public enum LpNormNormalizerTransformNormalizerKind : byte + { + L2Norm = 0, + StdDev = 1, + L1Norm = 2, + LInf = 3 + } - public sealed partial class NADropTransformColumn : OneToOneColumn, IOneToOneColumn + + public sealed partial class LpNormNormalizerTransformColumn : OneToOneColumn, IOneToOneColumn { + /// + /// The norm to use to normalize each sample + /// + public LpNormNormalizerTransformNormalizerKind? NormKind { get; set; } + + /// + /// Subtract mean from each value before normalizing + /// + public bool? SubMean { get; set; } + /// /// Name of the new column /// @@ -13329,16 +13580,15 @@ public sealed partial class NADropTransformColumn : OneToOneColumn - /// - public sealed partial class MissingValuesDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + public sealed partial class LpNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValuesDropper() + public LpNormalizer() { } - public MissingValuesDropper(params string[] inputColumns) + public LpNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -13349,7 +13599,7 @@ public MissingValuesDropper(params string[] inputColumns) } } - public MissingValuesDropper(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public LpNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -13362,23 +13612,33 @@ public MissingValuesDropper(params (string inputColumn, string outputColumn)[] i public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// Columns to drop the NAs for + /// New column definition(s) (optional form: name:src) /// - public NADropTransformColumn[] Column { get; set; } + public LpNormNormalizerTransformColumn[] Column { get; set; } + + /// + /// The norm to use to normalize each sample + /// + public LpNormNormalizerTransformNormalizerKind NormKind { get; set; } = LpNormNormalizerTransformNormalizerKind.L2Norm; + + /// + /// Subtract mean from each value before normalizing + /// + public bool SubMean { get; set; } = false; /// /// Input dataset @@ -13407,18 +13667,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValuesDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LpNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValuesDropperPipelineStep(output); + return new LpNormalizerPipelineStep(output); } - private class MissingValuesDropperPipelineStep : ILearningPipelineDataStep + private class LpNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValuesDropperPipelineStep(Output output) + public LpNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13433,123 +13693,49 @@ public MissingValuesDropperPipelineStep(Output output) namespace Transforms { - /// - /// - public sealed partial class MissingValuesRowDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel. + /// + public sealed partial class ManyHeterogeneousModelCombiner { /// - /// Column - /// - public string[] Column { get; set; } - - /// - /// If true, keep only rows that contain NA values, and filter the rest. + /// Transform model /// - public bool Complement { get; set; } = false; + public ArrayVar TransformModels { get; set; } = new ArrayVar(); /// - /// Input dataset + /// Predictor model /// - public Var Data { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output { /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model + /// Predictor model /// - public Var Model { get; set; } = new Var(); - - } - public Var GetInputData() => Data; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(MissingValuesRowDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - Data = dataStep.Data; - } - Output output = experiment.Add(this); - return new MissingValuesRowDropperPipelineStep(output); - } - - private class MissingValuesRowDropperPipelineStep : ILearningPipelineDataStep - { - public MissingValuesRowDropperPipelineStep(Output output) - { - Data = output.OutputData; - Model = output.Model; - } + public Var PredictorModel { get; set; } = new Var(); - public Var Data { get; } - public Var Model { get; } } } } namespace Transforms { - public enum NAReplaceTransformReplacementKind - { - DefaultValue = 0, - Mean = 1, - Minimum = 2, - Maximum = 3, - SpecifiedValue = 4 - } - - - public sealed partial class NAReplaceTransformColumn : OneToOneColumn, IOneToOneColumn - { - /// - /// Replacement value for NAs (uses default value if not given) - /// - public string ReplacementString { get; set; } - - /// - /// The replacement method to utilize - /// - public NAReplaceTransformReplacementKind? Kind { get; set; } - - /// - /// Whether to impute values by slot - /// - public bool? Slot { get; set; } - - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } - } - - /// - /// - public sealed partial class MissingValueSubstitutor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Normalizes the data based on the computed mean and variance of the data. + /// + public sealed partial class MeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueSubstitutor() + public MeanVarianceNormalizer() { } - public MissingValueSubstitutor(params string[] inputColumns) + public MeanVarianceNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -13560,7 +13746,7 @@ public MissingValueSubstitutor(params string[] inputColumns) } } - public MissingValueSubstitutor(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public MeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -13573,33 +13759,38 @@ public MissingValueSubstitutor(params (string inputColumn, string outputColumn)[ public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:rep:src) + /// Whether to use CDF as the output /// - public NAReplaceTransformColumn[] Column { get; set; } + public bool UseCdf { get; set; } = false; /// - /// The replacement method to utilize + /// New column definition(s) (optional form: name:src) /// - public NAReplaceTransformReplacementKind ReplacementKind { get; set; } = NAReplaceTransformReplacementKind.DefaultValue; + public NormalizeTransformAffineColumn[] Column { get; set; } /// - /// Whether to impute values by slot + /// Whether to map zero to zero, preserving sparsity /// - public bool ImputeBySlot { get; set; } = true; + public bool FixZero { get; set; } = true; + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -13628,18 +13819,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueSubstitutor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueSubstitutorPipelineStep(output); + return new MeanVarianceNormalizerPipelineStep(output); } - private class MissingValueSubstitutorPipelineStep : ILearningPipelineDataStep + private class MeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValueSubstitutorPipelineStep(Output output) + public MeanVarianceNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13655,40 +13846,768 @@ namespace Transforms { /// - /// Combines a sequence of TransformModels into a single model + /// Normalizes the data based on the observed minimum and maximum values of the data. /// - public sealed partial class ModelCombiner + public sealed partial class MinMaxNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - - /// - /// Input models - /// - public ArrayVar Models { get; set; } = new ArrayVar(); - - - public sealed class Output + public MinMaxNormalizer() { - /// - /// Combined model - /// - public Var OutputModel { get; set; } = new Var(); - } - } - } - - namespace Transforms - { - public enum NgramTransformWeightingCriteria - { - Tf = 0, - Idf = 1, - TfIdf = 2 - } - - - public sealed partial class NgramTransformColumn : OneToOneColumn, IOneToOneColumn + + public MinMaxNormalizer(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MinMaxNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:src) + /// + public NormalizeTransformAffineColumn[] Column { get; set; } + + /// + /// Whether to map zero to zero, preserving sparsity + /// + public bool FixZero { get; set; } = true; + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MinMaxNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MinMaxNormalizerPipelineStep(output); + } + + private class MinMaxNormalizerPipelineStep : ILearningPipelineDataStep + { + public MinMaxNormalizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + public enum NAHandleTransformReplacementKind + { + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3 + } + + + public sealed partial class NAHandleTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// The replacement method to utilize + /// + public NAHandleTransformReplacementKind? Kind { get; set; } + + /// + /// Whether to impute values by slot + /// + public bool? ImputeBySlot { get; set; } + + /// + /// Whether or not to concatenate an indicator vector column to the value column + /// + public bool? ConcatIndicator { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// + public sealed partial class MissingValueHandler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public MissingValueHandler() + { + } + + public MissingValueHandler(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MissingValueHandler(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:rep:src) + /// + public NAHandleTransformColumn[] Column { get; set; } + + /// + /// The replacement method to utilize + /// + public NAHandleTransformReplacementKind ReplaceWith { get; set; } = NAHandleTransformReplacementKind.DefaultValue; + + /// + /// Whether to impute values by slot + /// + public bool ImputeBySlot { get; set; } = true; + + /// + /// Whether or not to concatenate an indicator vector column to the value column + /// + public bool Concat { get; set; } = true; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MissingValueHandler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MissingValueHandlerPipelineStep(output); + } + + private class MissingValueHandlerPipelineStep : ILearningPipelineDataStep + { + public MissingValueHandlerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + public sealed partial class NAIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// + public sealed partial class MissingValueIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public MissingValueIndicator() + { + } + + public MissingValueIndicator(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MissingValueIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:src) + /// + public NAIndicatorTransformColumn[] Column { get; set; } + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MissingValueIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MissingValueIndicatorPipelineStep(output); + } + + private class MissingValueIndicatorPipelineStep : ILearningPipelineDataStep + { + public MissingValueIndicatorPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + public sealed partial class NADropTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// + public sealed partial class MissingValuesDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public MissingValuesDropper() + { + } + + public MissingValuesDropper(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MissingValuesDropper(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// Columns to drop the NAs for + /// + public NADropTransformColumn[] Column { get; set; } + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MissingValuesDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MissingValuesDropperPipelineStep(output); + } + + private class MissingValuesDropperPipelineStep : ILearningPipelineDataStep + { + public MissingValuesDropperPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// + public sealed partial class MissingValuesRowDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// Column + /// + public string[] Column { get; set; } + + /// + /// If true, keep only rows that contain NA values, and filter the rest. + /// + public bool Complement { get; set; } = false; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MissingValuesRowDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MissingValuesRowDropperPipelineStep(output); + } + + private class MissingValuesRowDropperPipelineStep : ILearningPipelineDataStep + { + public MissingValuesRowDropperPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + public enum NAReplaceTransformReplacementKind + { + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3, + SpecifiedValue = 4 + } + + + public sealed partial class NAReplaceTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Replacement value for NAs (uses default value if not given) + /// + public string ReplacementString { get; set; } + + /// + /// The replacement method to utilize + /// + public NAReplaceTransformReplacementKind? Kind { get; set; } + + /// + /// Whether to impute values by slot + /// + public bool? Slot { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// + public sealed partial class MissingValueSubstitutor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public MissingValueSubstitutor() + { + } + + public MissingValueSubstitutor(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MissingValueSubstitutor(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:rep:src) + /// + public NAReplaceTransformColumn[] Column { get; set; } + + /// + /// The replacement method to utilize + /// + public NAReplaceTransformReplacementKind ReplacementKind { get; set; } = NAReplaceTransformReplacementKind.DefaultValue; + + /// + /// Whether to impute values by slot + /// + public bool ImputeBySlot { get; set; } = true; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(MissingValueSubstitutor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new MissingValueSubstitutorPipelineStep(output); + } + + private class MissingValueSubstitutorPipelineStep : ILearningPipelineDataStep + { + public MissingValueSubstitutorPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Combines a sequence of TransformModels into a single model + /// + public sealed partial class ModelCombiner + { + + + /// + /// Input models + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + + public sealed class Output + { + /// + /// Combined model + /// + public Var OutputModel { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + public enum NgramTransformWeightingCriteria + { + Tf = 0, + Idf = 1, + TfIdf = 2 + } + + + public sealed partial class NgramTransformColumn : OneToOneColumn, IOneToOneColumn { /// /// Maximum ngram length @@ -15088,27 +16007,266 @@ public void AddColumn(string name, params string[] source) /// public bool OutputTokens { get; set; } = false; - /// - /// A dictionary of whitelisted terms. - /// - public TermLoaderArguments Dictionary { get; set; } + /// + /// A dictionary of whitelisted terms. + /// + public TermLoaderArguments Dictionary { get; set; } + + /// + /// Ngram feature extractor to use for words (WordBag/WordHashBag). + /// + [JsonConverter(typeof(ComponentSerializer))] + public NgramExtractor WordFeatureExtractor { get; set; } = new NGramNgramExtractor(); + + /// + /// Ngram feature extractor to use for characters (WordBag/WordHashBag). + /// + [JsonConverter(typeof(ComponentSerializer))] + public NgramExtractor CharFeatureExtractor { get; set; } = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false }; + + /// + /// Normalize vectors (rows) individually by rescaling them to unit norm. + /// + public TextTransformTextNormKind VectorNormalizer { get; set; } = TextTransformTextNormKind.L2; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(TextFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new TextFeaturizerPipelineStep(output); + } + + private class TextFeaturizerPipelineStep : ILearningPipelineDataStep + { + public TextFeaturizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// + public sealed partial class TextToKeyConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public TextToKeyConverter() + { + } + + public TextToKeyConverter(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public TextToKeyConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:src) + /// + public TermTransformColumn[] Column { get; set; } + + /// + /// Maximum number of terms to keep per column when auto-training + /// + public int MaxNumTerms { get; set; } = 1000000; + + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; + + /// + /// Whether key value metadata should be text, regardless of the actual input type + /// + public bool TextKeyValues { get; set; } = false; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(TextToKeyConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new TextToKeyConverterPipelineStep(output); + } + + private class TextToKeyConverterPipelineStep : ILearningPipelineDataStep + { + public TextToKeyConverterPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Split the dataset into train and test sets + /// + public sealed partial class TrainTestDatasetSplitter + { + + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + /// + /// Fraction of training data + /// + public float Fraction { get; set; } = 0.8f; + + /// + /// Stratification column + /// + public string StratificationColumn { get; set; } + + + public sealed class Output + { + /// + /// Training data + /// + public Var TrainData { get; set; } = new Var(); + + /// + /// Testing data + /// + public Var TestData { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + /// + public sealed partial class TreeLeafFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IFeaturizerInput, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + /// - /// Ngram feature extractor to use for words (WordBag/WordHashBag). + /// Output column: The suffix to append to the default column names /// - [JsonConverter(typeof(ComponentSerializer))] - public NgramExtractor WordFeatureExtractor { get; set; } = new NGramNgramExtractor(); + public string Suffix { get; set; } /// - /// Ngram feature extractor to use for characters (WordBag/WordHashBag). + /// If specified, determines the permutation seed for applying this featurizer to a multiclass problem. /// - [JsonConverter(typeof(ComponentSerializer))] - public NgramExtractor CharFeatureExtractor { get; set; } = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false }; + public int LabelPermutationSeed { get; set; } /// - /// Normalize vectors (rows) individually by rescaling them to unit norm. + /// Trainer to use /// - public TextTransformTextNormKind VectorNormalizer { get; set; } = TextTransformTextNormKind.L2; + public Var PredictorModel { get; set; } = new Var(); /// /// Input dataset @@ -15137,18 +16295,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TextFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(TreeLeafFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TextFeaturizerPipelineStep(output); + return new TreeLeafFeaturizerPipelineStep(output); } - private class TextFeaturizerPipelineStep : ILearningPipelineDataStep + private class TreeLeafFeaturizerPipelineStep : ILearningPipelineDataStep { - public TextFeaturizerPipelineStep(Output output) + public TreeLeafFeaturizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -15163,16 +16321,108 @@ public TextFeaturizerPipelineStep(Output output) namespace Transforms { - /// - /// - public sealed partial class TextToKeyConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + /// + /// Combines a TransformModel and a PredictorModel into a single PredictorModel. + /// + public sealed partial class TwoHeterogeneousModelCombiner { - public TextToKeyConverter() + + /// + /// Transform model + /// + public Var TransformModel { get; set; } = new Var(); + + /// + /// Predictor model + /// + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// Predictor model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + public sealed partial class VectorToImageTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Whether to use alpha channel + /// + public bool? ContainsAlpha { get; set; } + + /// + /// Whether to use red channel + /// + public bool? ContainsRed { get; set; } + + /// + /// Whether to use green channel + /// + public bool? ContainsGreen { get; set; } + + /// + /// Whether to use blue channel + /// + public bool? ContainsBlue { get; set; } + + /// + /// Whether to separate each channel or interleave in ARGB order + /// + public bool? InterleaveArgb { get; set; } + + /// + /// Width of the image + /// + public int? ImageWidth { get; set; } + + /// + /// Height of the image + /// + public int? ImageHeight { get; set; } + + /// + /// Offset (pre-scale) + /// + public float? Offset { get; set; } + + /// + /// Scale factor + /// + public float? Scale { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// Converts vector array into image type. + /// + public sealed partial class VectorToImage : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public VectorToImage() { } - public TextToKeyConverter(params string[] inputColumns) + public VectorToImage(params string[] inputColumns) { if (inputColumns != null) { @@ -15183,7 +16433,7 @@ public TextToKeyConverter(params string[] inputColumns) } } - public TextToKeyConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) + public VectorToImage(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { @@ -15196,15 +16446,15 @@ public TextToKeyConverter(params (string inputColumn, string outputColumn)[] inp public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -15212,27 +16462,52 @@ public void AddColumn(string outputColumn, string inputColumn) /// /// New column definition(s) (optional form: name:src) /// - public TermTransformColumn[] Column { get; set; } + public VectorToImageTransformColumn[] Column { get; set; } /// - /// Maximum number of terms to keep per column when auto-training + /// Whether to use alpha channel /// - public int MaxNumTerms { get; set; } = 1000000; + public bool ContainsAlpha { get; set; } = false; /// - /// List of terms + /// Whether to use red channel /// - public string[] Term { get; set; } + public bool ContainsRed { get; set; } = true; /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// Whether to use green channel /// - public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; + public bool ContainsGreen { get; set; } = true; /// - /// Whether key value metadata should be text, regardless of the actual input type + /// Whether to use blue channel /// - public bool TextKeyValues { get; set; } = false; + public bool ContainsBlue { get; set; } = true; + + /// + /// Whether to separate each channel or interleave in ARGB order + /// + public bool InterleaveArgb { get; set; } = false; + + /// + /// Width of the image + /// + public int ImageWidth { get; set; } + + /// + /// Height of the image + /// + public int ImageHeight { get; set; } + + /// + /// Offset (pre-scale) + /// + public float? Offset { get; set; } + + /// + /// Scale factor + /// + public float? Scale { get; set; } /// /// Input dataset @@ -15261,18 +16536,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TextToKeyConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(VectorToImage)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TextToKeyConverterPipelineStep(output); + return new VectorToImagePipelineStep(output); } - private class TextToKeyConverterPipelineStep : ILearningPipelineDataStep + private class VectorToImagePipelineStep : ILearningPipelineDataStep { - public TextToKeyConverterPipelineStep(Output output) + public VectorToImagePipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -15286,68 +16561,95 @@ public TextToKeyConverterPipelineStep(Output output) namespace Transforms { - - /// - /// Split the dataset into train and test sets - /// - public sealed partial class TrainTestDatasetSplitter + public enum WordEmbeddingsTransformPretrainedModelKind { + GloVe50D = 0, + GloVe100D = 1, + GloVe200D = 2, + GloVe300D = 3, + GloVeTwitter25D = 4, + GloVeTwitter50D = 5, + GloVeTwitter100D = 6, + GloVeTwitter200D = 7, + FastTextWikipedia300D = 8, + Sswe = 9 + } + public sealed partial class WordEmbeddingsTransformColumn : OneToOneColumn, IOneToOneColumn + { /// - /// Input dataset + /// Name of the new column /// - public Var Data { get; set; } = new Var(); + public string Name { get; set; } /// - /// Fraction of training data + /// Name of the source column /// - public float Fraction { get; set; } = 0.8f; + public string Source { get; set; } - /// - /// Stratification column - /// - public string StratificationColumn { get; set; } + } + /// + /// + public sealed partial class WordEmbeddings : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { - public sealed class Output + public WordEmbeddings() { - /// - /// Training data - /// - public Var TrainData { get; set; } = new Var(); - - /// - /// Testing data - /// - public Var TestData { get; set; } = new Var(); - } - } - } - - namespace Transforms - { + + public WordEmbeddings(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public WordEmbeddings(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } - /// - public sealed partial class TreeLeafFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IFeaturizerInput, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } /// - /// Output column: The suffix to append to the default column names + /// New column definition(s) (optional form: name:src) /// - public string Suffix { get; set; } + public WordEmbeddingsTransformColumn[] Column { get; set; } /// - /// If specified, determines the permutation seed for applying this featurizer to a multiclass problem. + /// Pre-trained model used to create the vocabulary /// - public int LabelPermutationSeed { get; set; } + public WordEmbeddingsTransformPretrainedModelKind? ModelKind { get; set; } = WordEmbeddingsTransformPretrainedModelKind.Sswe; /// - /// Trainer to use + /// Filename for custom word embedding model /// - public Var PredictorModel { get; set; } = new Var(); + public string CustomLookupTable { get; set; } /// /// Input dataset @@ -15376,18 +16678,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TreeLeafFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(WordEmbeddings)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TreeLeafFeaturizerPipelineStep(output); + return new WordEmbeddingsPipelineStep(output); } - private class TreeLeafFeaturizerPipelineStep : ILearningPipelineDataStep + private class WordEmbeddingsPipelineStep : ILearningPipelineDataStep { - public TreeLeafFeaturizerPipelineStep(Output output) + public WordEmbeddingsPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -15399,38 +16701,6 @@ public TreeLeafFeaturizerPipelineStep(Output output) } } - namespace Transforms - { - - /// - /// Combines a TransformModel and a PredictorModel into a single PredictorModel. - /// - public sealed partial class TwoHeterogeneousModelCombiner - { - - - /// - /// Transform model - /// - public Var TransformModel { get; set; } = new Var(); - - /// - /// Predictor model - /// - public Var PredictorModel { get; set; } = new Var(); - - - public sealed class Output - { - /// - /// Predictor model - /// - public Var PredictorModel { get; set; } = new Var(); - - } - } - } - namespace Transforms { diff --git a/src/Microsoft.ML/Data/TextLoader.cs b/src/Microsoft.ML/Data/TextLoader.cs index 6e89e8a54e..330412185e 100644 --- a/src/Microsoft.ML/Data/TextLoader.cs +++ b/src/Microsoft.ML/Data/TextLoader.cs @@ -7,6 +7,7 @@ using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using System; +using System.Collections.Generic; using System.Linq; using System.Reflection; using System.Text.RegularExpressions; @@ -71,20 +72,30 @@ public TextLoader CreateFrom(bool useHeader = false, char separator = '\t', bool allowQuotedStrings = true, bool supportSparse = true, bool trimWhitespace = false) { - var fields = typeof(TInput).GetFields(); - Arguments.Column = new TextLoaderColumn[fields.Length]; - for (int index = 0; index < fields.Length; index++) + var userType = typeof(TInput); + + var fieldInfos = userType.GetFields(BindingFlags.Public | BindingFlags.Instance); + + var propertyInfos = + userType + .GetProperties(BindingFlags.Public | BindingFlags.Instance) + .Where(x => x.CanRead && x.CanWrite && x.GetGetMethod() != null && x.GetSetMethod() != null && x.GetIndexParameters().Length == 0); + + var memberInfos = (fieldInfos as IEnumerable).Concat(propertyInfos).ToArray(); + + Arguments.Column = new TextLoaderColumn[memberInfos.Length]; + for (int index = 0; index < memberInfos.Length; index++) { - var field = fields[index]; - var mappingAttr = field.GetCustomAttribute(); + var memberInfo = memberInfos[index]; + var mappingAttr = memberInfo.GetCustomAttribute(); if (mappingAttr == null) - throw Contracts.Except($"{field.Name} is missing ColumnAttribute"); + throw Contracts.Except($"Field or property {memberInfo.Name} is missing ColumnAttribute"); if (Regex.Match(mappingAttr.Ordinal, @"[^(0-9,\*\-~)]+").Success) throw Contracts.Except($"{mappingAttr.Ordinal} contains invalid characters. " + $"Valid characters are 0-9, *, - and ~"); - var name = mappingAttr.Name ?? field.Name; + var name = mappingAttr.Name ?? memberInfo.Name; Runtime.Data.TextLoader.Range[] sources; if (!Runtime.Data.TextLoader.Column.TryParseSourceEx(mappingAttr.Ordinal, out sources)) @@ -96,8 +107,23 @@ public TextLoader CreateFrom(bool useHeader = false, tlc.Name = name; tlc.Source = new TextLoaderRange[sources.Length]; DataKind dk; - if (!TryGetDataKind(field.FieldType.IsArray ? field.FieldType.GetElementType() : field.FieldType, out dk)) - throw Contracts.Except($"{name} is of unsupported type."); + switch (memberInfo) + { + case FieldInfo field: + if (!TryGetDataKind(field.FieldType.IsArray ? field.FieldType.GetElementType() : field.FieldType, out dk)) + throw Contracts.Except($"Field {name} is of unsupported type."); + + break; + + case PropertyInfo property: + if (!TryGetDataKind(property.PropertyType.IsArray ? property.PropertyType.GetElementType() : property.PropertyType, out dk)) + throw Contracts.Except($"Property {name} is of unsupported type."); + break; + + default: + Contracts.Assert(false); + throw Contracts.ExceptNotSupp("Expected a FieldInfo or a PropertyInfo"); + } tlc.Type = dk; diff --git a/src/Microsoft.ML/Models/OnnxConverter.cs b/src/Microsoft.ML/Models/OnnxConverter.cs index c2b8e2b5c1..f4f9043b69 100644 --- a/src/Microsoft.ML/Models/OnnxConverter.cs +++ b/src/Microsoft.ML/Models/OnnxConverter.cs @@ -28,7 +28,7 @@ public sealed partial class OnnxConverter /// path defined through the Json option. /// /// This API supports the following arguments: - /// indicates the file to write the ONNX protocol buffer file to. This is optional. + /// indicates the file to write the ONNX protocol buffer file to. This is required. /// indicates the file to write the JSON representation of the ONNX model. This is optional. /// indicates the name property in the ONNX model. If left unspecified, it will /// be the extension-less name of the file specified in the onnx indicates the protocol buffer file diff --git a/src/Native/CMakeLists.txt b/src/Native/CMakeLists.txt index 767f6151fa..a0b06f864f 100644 --- a/src/Native/CMakeLists.txt +++ b/src/Native/CMakeLists.txt @@ -182,3 +182,4 @@ add_subdirectory(CpuMathNative) add_subdirectory(FastTreeNative) add_subdirectory(LdaNative) add_subdirectory(FactorizationMachineNative) +add_subdirectory(SymSgdNative) \ No newline at end of file diff --git a/src/Native/SymSgdNative/CMakeLists.txt b/src/Native/SymSgdNative/CMakeLists.txt new file mode 100644 index 0000000000..56baa7138d --- /dev/null +++ b/src/Native/SymSgdNative/CMakeLists.txt @@ -0,0 +1,21 @@ +project (SymSgdNative) + +set(SOURCES + SymSgdNative.cpp +) + +find_library(MKL_LIBRARY MklImports HINTS ${MKL_LIB_PATH}) +if(NOT WIN32) + list(APPEND SOURCES ${VERSION_FILE_PATH}) + if(NOT APPLE) + SET(CMAKE_SKIP_BUILD_RPATH FALSE) + SET(CMAKE_BUILD_WITH_INSTALL_RPATH FALSE) + SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE) + SET(CMAKE_INSTALL_RPATH "$ORIGIN/") + endif() +endif() + +add_library(SymSgdNative SHARED ${SOURCES} ${RESOURCES}) +target_link_libraries(SymSgdNative PUBLIC ${MKL_LIBRARY}) + +install_library_and_symbols (SymSgdNative) \ No newline at end of file diff --git a/src/Native/SymSgdNative/Macros.h b/src/Native/SymSgdNative/Macros.h new file mode 100644 index 0000000000..728dbe3efa --- /dev/null +++ b/src/Native/SymSgdNative/Macros.h @@ -0,0 +1,9 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#define MIN(__X__, __Y__) (((__X__) > (__Y__)) ? (__Y__) : (__X__)) + +// This is a very large prime number used for permutation +#define VERYLARGEPRIME 961748941 \ No newline at end of file diff --git a/src/Native/SymSgdNative/SparseBLAS.h b/src/Native/SymSgdNative/SparseBLAS.h new file mode 100644 index 0000000000..6d13e9cff2 --- /dev/null +++ b/src/Native/SymSgdNative/SparseBLAS.h @@ -0,0 +1,31 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include "../Stdafx.h" + +extern "C" float cblas_sdot(const int vecSize, const float* denseVecX, const int incX, const float* denseVecY, const int incY); +extern "C" float cblas_sdoti(const int sparseVecSize, const float* sparseVecValues, const int* sparseVecIndices, float* denseVec); +extern "C" void cblas_saxpy(const int vecSize, const float coef, const float* denseVecX, const int incX, float* denseVecY, const int incY); +extern "C" void cblas_saxpyi(const int sparseVecSize, const float coef, const float* sparseVecValues, const int* sparseVecIndices, float* denseVec); + +float SDOT(const int vecSize, const float* denseVecX, const float* denseVecY) +{ + return cblas_sdot(vecSize, denseVecX, 1, denseVecY, 1); +} + +float SDOTI(const int sparseVecSize, const int* sparseVecIndices, const float* sparseVecValues, float* denseVec) +{ + return cblas_sdoti(sparseVecSize, sparseVecValues, sparseVecIndices, denseVec); +} + +void SAXPY(const int vecSize, const float* denseVecX, float* denseVecY, float coef) +{ + return cblas_saxpy(vecSize, coef, denseVecX, 1, denseVecY, 1); +} + +void SAXPYI(const int sparseVecSize, const int* sparseVecIndices, const float* sparseVecValues, float* denseVec, float coef) +{ + cblas_saxpyi(sparseVecSize, coef, sparseVecValues, sparseVecIndices, denseVec); +} \ No newline at end of file diff --git a/src/Native/SymSgdNative/SymSgdNative.cpp b/src/Native/SymSgdNative/SymSgdNative.cpp new file mode 100644 index 0000000000..76a1314cf2 --- /dev/null +++ b/src/Native/SymSgdNative/SymSgdNative.cpp @@ -0,0 +1,483 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +// SymSGDNative.cpp : Defines the exported functions for the DLL application. + +#include +#include +#if defined(USE_OMP) +#include +#endif +#include +#include "../Stdafx.h" +#include "Macros.h" +#include "SparseBLAS.h" +#include "SymSgdNative.h" + +// This method learns for a single instance +inline void LearnInstance(int instSize, int* instIndices, float* instValues, + float label, float alpha, float l2Const, float piw, float& weightScaling, float* weightVector, float& bias) +{ + float dotProduct = 0.0f; + if (instIndices) // If it is a sparse instance + dotProduct = SDOTI(instSize, instIndices, instValues, weightVector) * weightScaling + bias; + else // If it is dense case. + dotProduct = SDOT(instSize, instValues, weightVector)*weightScaling + bias; + // Compute the derivative coefficient + float sigmoidPrediction = 1.0f / (1.0f + exp(-dotProduct)); + float derivative = (label > 0) ? piw * (sigmoidPrediction - 1) : sigmoidPrediction; + float derivativeCoef = -alpha * derivative; + weightScaling *= (1.0f - alpha*l2Const); + // Apply the derivative back to the weightVector + if (instIndices) // If it is a sparse instance + SAXPYI(instSize, instIndices, instValues, weightVector, derivativeCoef / weightScaling); + else + SAXPY(instSize, instValues, weightVector, derivativeCoef / weightScaling); + + bias = bias + derivativeCoef; +} + +// This method permutes frequent features with starting consecutive features +void ComputeRemapping(int totalNumInstances, int* instSizes, int** instIndices, + int numFeat, int numLocIter, int numThreads, SymSGDState* state, int& numFreqFeat) +{ + // There are two maps used for this permutation: + // 1) a direct map + state->FreqFeatDirectMap = new int[numFeat]; + // 2) an unordered map + state->FreqFeatUnorderedMap = new std::unordered_map(); + + int* freqFeatDirectMap = state->FreqFeatDirectMap; + std::unordered_map* freqFeatUnorderedMap = (std::unordered_map*)state->FreqFeatUnorderedMap; + + // If numLocIter is 1, it means that every iteration must be reduced and therefore, no feature should be considered frequent + // In this special case, we do not need a mapping since it is identity. + if (numLocIter == 1) + { + memset(freqFeatDirectMap, -1, sizeof(int)*numFeat); + numFreqFeat = 0; + return; + } + + memset(freqFeatDirectMap, 0, sizeof(int)*numFeat); + + // Frequent features are searched by subSampling the data and histogramming the frequecy of each feature + int subSampleSize = MIN(1000 * numLocIter*numThreads, totalNumInstances); + // The threshold to call a feature frequent + float threshold = (float)subSampleSize / (float)numLocIter; + + // Compute the histogram for the frequency of features + // freqFeatDirectMap is used to store the histogram + for (int i = 0; i < subSampleSize; i++) + { + if (instIndices[i]) + { + for (int j = 0; j < instSizes[i]; j++) { + freqFeatDirectMap[instIndices[i][j]]++; + } + } else + { + for (int j = 0; j < numFeat; j++) { + freqFeatDirectMap[j]++; + } + } + } + // Compute the permutation that is required to re-order features such that + // frequent features are at the beginning of feature space + + // feature i and feautre numFreqFeat are subject to swap. The only difficulty is when + // numFreqFeat is already recognized as a frequent feature. + // Variable numFreqFeat keeps the numFreqFeat observed so far + numFreqFeat = 0; + for (int i = 0; i < numFeat; i++) + { + // Check if i is a frequent feature + if (freqFeatDirectMap[i] > threshold) + { + // Check if all the features seen so far are all frequent + if (numFreqFeat != i) + { + // We have to swap i with numFreqFeat + auto searchedRes = freqFeatUnorderedMap->find(numFreqFeat); + // Check if numFreqFeat is already occupied in freqFeatureUnorderedMap + // which means that numFreqFeat is already a frequent feature + if (searchedRes == freqFeatUnorderedMap->end()) + { + // In this case, numFreqFeature is unoccupied and can be used for permutation for i. + (*freqFeatUnorderedMap)[i] = numFreqFeat; + (*freqFeatUnorderedMap)[numFreqFeat] = i; + } else + { + // Since numFreqFeat is already a frequent feature, its mapped index, (*freqFeatUnorderedMap)[numFreqFeat], + // was a non-frequent feature. So in this case, numFreqFeat mapping is removed and its mapped feature index + // is used for i instead. + int oldFreqFeat = numFreqFeat; + int oldFreqFeatMappedTo = searchedRes->second; + freqFeatUnorderedMap->erase(searchedRes); + (*freqFeatUnorderedMap)[oldFreqFeatMappedTo] = i; + (*freqFeatUnorderedMap)[i] = oldFreqFeatMappedTo; + } + } + numFreqFeat++; + } + // freqFeatDirectMap is a direct map and -1 means freqFeatDirectMap[i] = i (identity mapping) + freqFeatDirectMap[i] = -1; + } + + // Here, using the unordered_map, we set the direct map accordingly + auto endOfUnorderedMap = freqFeatUnorderedMap->end(); + for (auto it = freqFeatUnorderedMap->begin(); it != endOfUnorderedMap; it++) + { + freqFeatDirectMap[it->first] = it->second; + } +} + +// This method, remap an instance using the maps provided by ComputeRemapping +void RemapInstances(int* instSizes, int** instIndices, float** instValues, int myStart, int myEnd, SymSGDState* state) +{ + int* freqFeatDirectMap = state->FreqFeatDirectMap; + std::unordered_map* freqFeatUnorderedMap = (std::unordered_map*)state->FreqFeatUnorderedMap; + auto itBegin = freqFeatUnorderedMap->begin(); + auto itEnd = freqFeatUnorderedMap->begin(); + for (int j = myStart; j < myEnd; j++) + { + int instSize = instSizes[j]; + // Check if instance is sparse + if (instIndices[j]) + { + // Just swap the indices accordingly + for (int k = 0; k < instSize; k++) + { + int oldIndex = instIndices[j][k]; + // Direct map is used here since it is much more efficient to access it + auto mappedIndex = freqFeatDirectMap[oldIndex]; + if (mappedIndex != -1) + { + int newIndex = mappedIndex; + instIndices[j][k] = newIndex; + } + } + } else + { + // If the instance is dense, we have to swap the values instead + for (auto it = itBegin; it != itEnd; it++) + { + float temp = instValues[j][it->second]; + instValues[j][it->second] = instValues[j][it->first]; + instValues[j][it->first] = temp; + } + } + } +} + +float MaxPossibleAlpha(float alpha, float l2Const, int totalNumInstances) +{ + return (1.0f - pow(10.0f, -6.0f / (float)totalNumInstances)) / l2Const; +} + +void TuneAlpha(float& alpha, float l2Const, int totalNumInstances, int* instSizes, int** instIndices, + float** instValues, int numFeat, int numThreads) +{ + alpha = 1e0f; + int logSqrtNumInst = (int) round(log10(sqrt(totalNumInstances)))-3; + //int logAverageNorm = int(log10(averageNorm)); + if (logSqrtNumInst > 0) + for (int i = 0; i < logSqrtNumInst; i++) + alpha = alpha / 10.0f; + else if (logSqrtNumInst < 0) + for (int i = 0; i < -logSqrtNumInst; i++) + alpha = alpha * 10.0f; + + // If we have l2Const > 0, we want to make sure alpha is not too large + if (l2Const > 0) + { + // Since weightScaling is multiplied by (1-alpha*lambda), + // we should make sure (1-alpha*lamda)^totalNumInstances > 1e-6 which is + // the threshold for applying the weightScaling. Therefore, + // alpha < (1-10^(-6/totalNumInstances))/l2Const. + alpha = MIN(alpha, MaxPossibleAlpha(alpha, l2Const, totalNumInstances)); + } + + printf("Initial learning rate is tuned to %f\n", alpha); +} + + +void TuneNumLocIter(int& numLocIter, int totalNumInstances, int* instSizes, int numThreads) +{ + int averageInstSizes = 0; + for (int i = 0; i < totalNumInstances; i++) + averageInstSizes += instSizes[i]; + averageInstSizes = averageInstSizes / totalNumInstances; + + if (averageInstSizes > 1000) + numLocIter = 40 / numThreads; + else + numLocIter = 160 / numThreads; +} + + +// This method sets SymSGDState and computes Remapping for indices and allocates the +// required memory for SymSGD learners. +void InitializeState(int totalNumInstances, int* instSizes, int** instIndices, float** instValues, + int numFeat, bool tuneNumLocIter, int& numLocIter, int numThreads, bool tuneAlpha, float& alpha, + float l2Const, SymSGDState* state) +{ + if (tuneAlpha) + { + TuneAlpha(alpha, l2Const, totalNumInstances, instSizes, instIndices, instValues, numFeat, numThreads); + } else + { + // Check if user alpha is too large because of l2Const. Check the comment about positive l2Const in TuneAlpha. + float maxPossibleAlpha = MaxPossibleAlpha(alpha, l2Const, totalNumInstances); + if (alpha > maxPossibleAlpha) + printf("Warning: learning rate is too high! Try using a value < %e instead\n", maxPossibleAlpha); + } + + if (tuneNumLocIter) + TuneNumLocIter(numLocIter, totalNumInstances, instSizes, numThreads); + + state->WeightScaling = 1.0f; +#if defined(USE_OMP) + if (numThreads > 1) + { + state->PassIteration = 0; + state->NumFrequentFeatures = 0; + state->TotalInstancesProcessed = 0; + ComputeRemapping(totalNumInstances, instSizes, instIndices, numFeat, + numLocIter, numThreads, state, state->NumFrequentFeatures); + printf("Number of frequent features: %d\nNumber of features: %d\n", state->NumFrequentFeatures, numFeat); + + state->NumLearners = numThreads; + state->Learners = new SymSGD*[numThreads]; + SymSGD** learners = (SymSGD**)(state->Learners); + + // Allocation of SymSGD learners happens in parallel to follow the first touch policy. + #pragma omp parallel num_threads(numThreads) + { + int threadId = omp_get_thread_num(); + learners[threadId] = new SymSGD(state->NumFrequentFeatures, threadId); + } + } + + // To make sure that MKL runs sequentially + omp_set_num_threads(1); +#endif +} + +float Loss(int instSize, int* instIndices, float* instValues, + float label, float piw, float& weightScaling, float* weightVector, float& bias) +{ + float dotProduct = 0.0f; + if (instIndices) // If it is a sparse instance + dotProduct = SDOTI(instSize, instIndices, instValues, weightVector) * weightScaling + bias; + else // If it is dense case. + dotProduct = SDOT(instSize, instValues, weightVector) * weightScaling + bias; + float sigmoidPrediction = 1.0f / (1.0f + exp(-dotProduct)); + float loss = (label > 0) ? -log2(sigmoidPrediction) : -log2(1 - sigmoidPrediction); + // To prevent from loss going to infinity + if (loss > 100.0f) + loss = 100.0f; + if (label > 0) + loss *= piw; + return loss; +} + +// This methdo learns for loaded instance for as many passes as demanded +// Note that InitializeState should be called before this method +EXPORT_API(void) LearnAll(int totalNumInstances, int* instSizes, int** instIndices, float** instValues, + float* labels, bool tuneAlpha, float& alpha, float l2Const, float piw, float* weightVector, + float& bias, int numFeat, int numPasses, int numThreads, bool tuneNumLocIter, int& numLocIter, float tolerance, + bool needShuffle, bool shouldInitialize, SymSGDState* state) +{ + // If this is the first time LearnAll is called, initialize it. + if (shouldInitialize) + InitializeState(totalNumInstances, instSizes, instIndices, instValues, numFeat, tuneNumLocIter, numLocIter, numThreads, tuneAlpha, alpha, l2Const, state); + float& weightScaling = state->WeightScaling; + + float totalAverageLoss = 0.0f; // Reserved for total loss computation + float oldAverageLoss = INFINITY; + float olderAverageLoss = INFINITY; + float oldestAverageLoss = INFINITY; + float totalOverallAverageLoss = INFINITY; + + float adjustedAlpha = alpha; + // Check if totalNumInstances is too small to run in parallel + if (numThreads == 1 || totalNumInstances < numThreads) + { + // For i=[0..totalNumInstances-1], (curPermMultiplier*i) % totalNumInstances always creates a pseduo random permutation + int64_t curPermMultiplier = (VERYLARGEPRIME % totalNumInstances); + // In the sequential case, just apply normal SGD + for (int i = 0; i < numPasses; i++) + { + for (int j = 0; j < totalNumInstances; j++) + { + int64_t index = j; + if (needShuffle) + index = (((int64_t)index * (int64_t)curPermMultiplier) % (int64_t)totalNumInstances); + // alpha decays with the square root of number of instances processed. + float thisAlpha = adjustedAlpha / (float)sqrt(1 + state->PassIteration * totalNumInstances + j); + LearnInstance(instSizes[index], instIndices[index], instValues[index], labels[index], thisAlpha, l2Const, piw, + weightScaling, weightVector, bias); + //state->TotalInstancesProcessed++; + if (weightScaling < 1e-6) + { + for (int k = 0; k < numFeat; k++) + { + weightVector[k] *= weightScaling; + } + weightScaling = 1.0f; + } + } + + float averageLoss = 0.0f; + // Computing the total loss + for (int j = 0; j < totalNumInstances; j++) + averageLoss += Loss(instSizes[j], instIndices[j], instValues[j], labels[j], piw, weightScaling, weightVector, bias); + averageLoss = averageLoss / (float)totalNumInstances; + // If we the loss did not improve the learning rate was high, decay it. + if (tuneAlpha && oldAverageLoss - averageLoss < tolerance) + adjustedAlpha = adjustedAlpha / 10.0f; + float overallAverageLoss = oldestAverageLoss - averageLoss; + oldestAverageLoss = olderAverageLoss; + olderAverageLoss = oldAverageLoss; + oldAverageLoss = averageLoss; + + averageLoss = 0.0f; + // Terminate if average loss difference between current model and the model from 3 passes ago is small + if (overallAverageLoss < tolerance) + break; + + // For shuffling in the next passes, instead of curPermMultiplier, use curPermMultiplier^2 which has exactly the same effect + if (needShuffle) + curPermMultiplier = (((int64_t)curPermMultiplier * (int64_t)curPermMultiplier) % (int64_t)totalNumInstances); + state->PassIteration++; + } + } else + { +#if defined(USE_OMP) + // In parallel case... + bool shouldRemap = !((std::unordered_map*)state->FreqFeatUnorderedMap)->empty(); + SymSGD** learners = (SymSGD**)(state->Learners); + + float oldWeightScaling = 1.0f; + #pragma omp parallel num_threads(numThreads) + { + int threadId = omp_get_thread_num(); + // Compute the portion of instances associated with threadId + int myStart = (totalNumInstances * threadId) / numThreads; + int myEnd = (totalNumInstances * (threadId + 1)) / numThreads; + int myRangeLength = myEnd - myStart; + + if (shouldRemap) + RemapInstances(instSizes, instIndices, instValues, myStart, myEnd, state); + + // This variable is used to keep track of how many instances are learned so far to do a reduction + int instancesLearnedSinceReduction = 0; + + learners[threadId]->ResetModel(bias, weightVector, weightScaling); + int64_t curPermMultiplier = (VERYLARGEPRIME % myRangeLength); + + for (int i = 0; i < numPasses; i++) + { + for (int j = 0; j < myRangeLength; j++) + { + int64_t index = myStart + j; + if (needShuffle) + index = myStart + (((int64_t)j * (int64_t)curPermMultiplier) % (int64_t)myRangeLength); + // alpha decays with the square root of number of instances processed. + float thisAlpha = adjustedAlpha / (float)sqrt(1 + state->PassIteration*totalNumInstances + j*numThreads); + learners[threadId]->LearnLocalModel(instSizes[index], instIndices[index], instValues[index], labels[index], + thisAlpha, l2Const, piw, weightVector); + instancesLearnedSinceReduction++; + // If it reached numLocIter, do a reduction + if (instancesLearnedSinceReduction == numLocIter) + { + learners[threadId]->Reduction(weightVector, bias, weightScaling); + learners[threadId]->ResetModel(bias, weightVector, weightScaling); + instancesLearnedSinceReduction = 0; + } + } + + // Check if we need to reweight the weight vector + if (l2Const > 0.0f) + { + #pragma omp barrier + if (weightScaling < 1e-6) + { + #pragma omp for + for (int featIndex = 0; featIndex < numFeat; featIndex++) + weightVector[featIndex] *= weightScaling; + if (threadId == 0) + weightScaling = 1.0f; + } + } + + #pragma omp barrier + #pragma omp for reduction(+:totalAverageLoss) + for (int j = 0; j < totalNumInstances; j++) + totalAverageLoss += Loss(instSizes[j], instIndices[j], instValues[j], labels[j], piw, weightScaling, weightVector, bias); + #pragma omp barrier + if (threadId == 0) + { + totalAverageLoss = totalAverageLoss / (float)totalNumInstances; + // If we the loss did not improve the learning rate was high, decay it. + if (tuneAlpha && oldAverageLoss - totalAverageLoss < tolerance) + adjustedAlpha = adjustedAlpha / 10.0f; + state->PassIteration++; + totalOverallAverageLoss = oldestAverageLoss - totalAverageLoss; + oldestAverageLoss = olderAverageLoss; + olderAverageLoss = oldAverageLoss; + oldAverageLoss = totalAverageLoss; + + totalAverageLoss = 0.0f; + } + #pragma omp barrier + // Terminate if average loss difference between current model and the model from 3 passes ago is small + if (totalOverallAverageLoss < tolerance) + break; + + if (needShuffle) + curPermMultiplier = (((int64_t)curPermMultiplier * (int64_t)curPermMultiplier) % (int64_t)myRangeLength); + } + } + state->TotalInstancesProcessed += numPasses*totalNumInstances; +#endif + } +} + +// This method maps back the weight vector to the original feature space +EXPORT_API(void) MapBackWeightVector(float* weightVector, SymSGDState* state) +{ + std::unordered_map* freqFeatUnorderedMap = (std::unordered_map*)state->FreqFeatUnorderedMap; + auto endOfUnorderedMap = freqFeatUnorderedMap->end(); + for (auto it = freqFeatUnorderedMap->begin(); it != endOfUnorderedMap; it++) + { + if (it->first < it->second) + { + float temp = weightVector[it->second]; + weightVector[it->second] = weightVector[it->first]; + weightVector[it->first] = temp; + } + } +} + +// Deallocation method +EXPORT_API(void) DeallocateSequentially(SymSGDState* state) +{ +#if defined(USE_OMP) + // To make sure that for the rest of MKL calls use parallelism + omp_set_num_threads(omp_get_num_procs()); +#endif + + SymSGD** learners = (SymSGD**)(state->Learners); + if (learners) + { + for (int i = 0; i < state->NumLearners; i++) + delete learners[i]; + } + if (state->FreqFeatUnorderedMap) + delete (std::unordered_map*)state->FreqFeatUnorderedMap; + if (state->FreqFeatDirectMap) + delete[] state->FreqFeatDirectMap; +} \ No newline at end of file diff --git a/src/Native/SymSgdNative/SymSgdNative.h b/src/Native/SymSgdNative/SymSgdNative.h new file mode 100644 index 0000000000..48ab3b0a75 --- /dev/null +++ b/src/Native/SymSgdNative/SymSgdNative.h @@ -0,0 +1,145 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include "../Stdafx.h" + +using namespace std; + +// In almost every sparse dataset, there is a great imbalance in frequency of features +// This class learns a local model for frequent features and modifies the global model for non-frequent features +// and only applies the frequent features it learned locally to the global model after certain number of iterations +class SymSGD +{ +private: + int _numFreqFeat; + // Local models that is learned + float* _localModel; + // A copy of the local model when it started learning + float* _startModel; + + // Bias is by default a frequent feature + float _bias, _startBias; + + // Local weightScaling for L2 regularization + float _weightScaling, _startWeightScaling; +public: + SymSGD(int numFreqFeat, int seed) + { + _numFreqFeat = numFreqFeat; + if (numFreqFeat > 0) + { + _localModel = new float[numFreqFeat]; + _startModel = new float[numFreqFeat]; + } else + { + _localModel = NULL; + _startModel = NULL; + } + } + + ~SymSGD() + { + if (_numFreqFeat > 0) { + delete[] _localModel; + delete[] _startModel; + } + } + + // Learns for the local model on frequent features and global model for non-frequent features + void LearnLocalModel(int instSize, int * instIndices, + float * instValues, float instLabel, float alpha, float l2Const, float piw, float* globModel) +{ + float dotProduct = 0.0f; + // Check if it is a sparse instance + if (instIndices) + { + for (int i = 0; i < instSize; i++) + { + int curIndex = instIndices[i]; + if (curIndex < _numFreqFeat) + { + // dotProduct on freqeunt features are computed with local model + dotProduct += _localModel[curIndex] * instValues[i]; + } else + { + // Otherwise on global model + dotProduct += globModel[curIndex] * instValues[i]; + } + } + } else + { + // In dense case scenario, there is no need to check on indices + dotProduct += SDOT(_numFreqFeat, &_localModel[0], instValues) + + SDOT(instSize - _numFreqFeat, &globModel[_numFreqFeat], &instValues[_numFreqFeat]); + } + dotProduct = dotProduct*_weightScaling + _bias; + + _weightScaling *= (1.0f - alpha*l2Const); + + // Compute the derivative coefficient + float sigmoidPrediction = 1.0f / (1.0f + exp(-dotProduct)); + float derivative = (instLabel > 0) ? (sigmoidPrediction - 1) : sigmoidPrediction; + if (instLabel > 0) + derivative *= piw; + float derivativeCoef = -2 * alpha * derivative; + float derivativeWeightScaledCoef = derivativeCoef / _weightScaling; + if (instIndices) + { + for (int i = 0; i < instSize; i++) + { + int curIndex = instIndices[i]; + if (curIndex < _numFreqFeat) + { // Apply the gradient to the local model for frequent features + _localModel[curIndex] += derivativeWeightScaledCoef * instValues[i]; + } else // Apply the gradient to the global model for non-frequent features + { + globModel[curIndex] += derivativeWeightScaledCoef * instValues[i]; + } + } + } else + { + // In dense case scenario, there is no need to check on indices + SAXPY(_numFreqFeat, instValues, &_localModel[0], derivativeWeightScaledCoef); + SAXPY(instSize - _numFreqFeat, &instValues[_numFreqFeat], &globModel[_numFreqFeat], derivativeWeightScaledCoef); + } + _bias = _bias + derivativeCoef; + } + + // This method copies the global models to _localModel and _startModel + void ResetModel(float bias, float* globModel, float weightScaling) + { + memcpy(&_localModel[0], globModel, _numFreqFeat * sizeof(float)); + memcpy(&_startModel[0], &_localModel[0], _numFreqFeat * sizeof(float)); + + _bias = bias; + _startBias = bias; + _weightScaling = weightScaling; + _startWeightScaling = weightScaling; + } + + // Adds the delta of the _localModel and _startModel to the global model for frequent features + void Reduction(float* globModel, float& bias, float& weightScaling) + { + for (int i = 0; i < _numFreqFeat; i++) + { + globModel[i] += _localModel[i] - _startModel[i]; + } + bias += _bias - _startBias; + weightScaling *= (_weightScaling / _startWeightScaling); + } +}; + +// The state that is shared between SymSGD and SymSGDNative +struct SymSGDState +{ + int NumLearners; + int TotalInstancesProcessed; + void* Learners; + void* FreqFeatUnorderedMap; + int* FreqFeatDirectMap; + int NumFrequentFeatures; + int PassIteration; + float WeightScaling; +}; \ No newline at end of file diff --git a/src/Native/build.cmd b/src/Native/build.cmd index e2bbc3a4dc..11684ac9d2 100644 --- a/src/Native/build.cmd +++ b/src/Native/build.cmd @@ -13,16 +13,21 @@ set __IntermediatesDir="" set __BuildArch=x64 set __VCBuildArch=x86_amd64 set CMAKE_BUILD_TYPE=Debug +set MKL_LIB_PATH="" :Arg_Loop if [%1] == [] goto :ToolsVersion if /i [%1] == [Release] ( set CMAKE_BUILD_TYPE=Release&&shift&goto Arg_Loop) +if /i [%1] == [Release-Intrinsics] ( set CMAKE_BUILD_TYPE=Release-Intrinsics&&shift&goto Arg_Loop) if /i [%1] == [Debug] ( set CMAKE_BUILD_TYPE=Debug&&shift&goto Arg_Loop) +if /i [%1] == [Debug-Intrinsics] ( set CMAKE_BUILD_TYPE=Debug-Intrinsics&&shift&goto Arg_Loop) if /i [%1] == [x86] ( set __BuildArch=x86&&set __VCBuildArch=x86&&shift&goto Arg_Loop) if /i [%1] == [x64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) if /i [%1] == [amd64] ( set __BuildArch=x64&&set __VCBuildArch=x86_amd64&&shift&goto Arg_Loop) +if /i [%1] == [--mkllibpath] ( set MKL_LIB_PATH=%2&&shift&goto Arg_Loop) + shift goto :Arg_Loop @@ -86,6 +91,10 @@ if %__IntermediatesDir% == "" ( set "__CMakeBinDir=%__CMakeBinDir:\=/%" set "__IntermediatesDir=%__IntermediatesDir:\=/%" +:: Strip the "-Intrinsics" suffix from the build type +if [%CMAKE_BUILD_TYPE:~-11%] == [-Intrinsics] ( + set CMAKE_BUILD_TYPE=%CMAKE_BUILD_TYPE:~0,-11% +) :: Check that the intermediate directory exists so we can place our cmake build tree there if not exist "%__IntermediatesDir%" md "%__IntermediatesDir%" diff --git a/src/Native/build.proj b/src/Native/build.proj index 41611a24ff..ab338ac855 100644 --- a/src/Native/build.proj +++ b/src/Native/build.proj @@ -25,12 +25,30 @@ true + + $(BaseOutputPath)$(TargetArchitecture).$(Configuration)\Native + + lib + .dll + .so + .dylib + + .pdb + .so.dbg + .dylib.dwarf + + win + linux + osx + $(PackageRid)-$(TargetArchitecture) + + --stripSymbols - --configuration $(Configuration) --arch $(TargetArchitecture) $(StripArgs) + --configuration $(Configuration) --arch $(TargetArchitecture) $(StripArgs) --mkllibpath $(PackagesDir)mlnetmkldeps/$(MlNetMklDepsPackageVersion)/runtimes/$(PackageRid)/native @@ -41,9 +59,9 @@ - + - $(Configuration) $(TargetArchitecture) + $(Configuration) $(TargetArchitecture) --mkllibpath $(PackagesDir)mlnetmkldeps\$(MlNetMklDepsPackageVersion)\runtimes\$(PackageRid)\native @@ -53,25 +71,12 @@ - - - $(BaseOutputPath)$(TargetArchitecture).$(Configuration)\Native - - lib - .dll - .so - .dylib - - .pdb - .so.dbg - .dylib.dwarf - - win - linux - osx - $(PackageRid)-$(TargetArchitecture) - - + + + + + @@ -90,6 +95,8 @@ RelativePath="Microsoft.ML\runtimes\$(PackageRid)\native" /> + @@ -98,6 +105,11 @@ Include="@(NativePackageAsset->'%(RootDir)%(Directory)%(Filename)$(NativeLibSymbolExtension)')" /> + + + + diff --git a/src/Native/build.sh b/src/Native/build.sh index 7f5b835cad..d508bfbf04 100755 --- a/src/Native/build.sh +++ b/src/Native/build.sh @@ -9,7 +9,7 @@ usage() echo " --arch Target Architecture (x64, x86)" echo " --configuration Build Configuration (Debug, Release)" echo " --stripSymbols Enable symbol stripping (to external file)" - + echo " --mkllibpath Path to mkl library." exit 1 } @@ -28,6 +28,7 @@ __configuration=Debug __rootBinPath="$RootRepo/bin" __baseIntermediateOutputPath="$__rootBinPath/obj" __versionSourceFile="$__baseIntermediateOutputPath/version.c" +__mkllibpath="" while [ "$1" != "" ]; do lowerI="$(echo $1 | awk '{print tolower($0)}')" @@ -44,6 +45,10 @@ while [ "$1" != "" ]; do shift __configuration=$1 ;; + --mkllibpath) + shift + __mkllibpath=$1 + ;; --stripsymbols) __strip_argument="-DSTRIP_SYMBOLS=true" ;; @@ -53,7 +58,7 @@ while [ "$1" != "" ]; do shift done -__cmake_defines="-DCMAKE_BUILD_TYPE=${__configuration} ${__strip_argument}" +__cmake_defines="-DCMAKE_BUILD_TYPE=${__configuration} ${__strip_argument} -DMKL_LIB_PATH=${__mkllibpath}" __IntermediatesDir="$__baseIntermediateOutputPath/$__build_arch.$__configuration/Native" __BinDir="$__rootBinPath/$__build_arch.$__configuration/Native" @@ -97,4 +102,4 @@ echo "Building Machine Learning native components from $DIR to $(pwd)" set -x # turn on trace cmake "$DIR" -G "Unix Makefiles" $__cmake_defines set +x # turn off trace -make install +make install \ No newline at end of file diff --git a/src/Native/gen-buildsys-win.bat b/src/Native/gen-buildsys-win.bat index 2b3a6e4f25..0750995dcf 100644 --- a/src/Native/gen-buildsys-win.bat +++ b/src/Native/gen-buildsys-win.bat @@ -27,7 +27,7 @@ for /f "delims=" %%a in ('powershell -NoProfile -ExecutionPolicy ByPass "& .\pro popd :DoGen -"%CMakePath%" "-DCMAKE_BUILD_TYPE=%CMAKE_BUILD_TYPE%" "-DCMAKE_INSTALL_PREFIX=%__CMakeBinDir%" -G "Visual Studio %__VSString%" -B. -H%1 +"%CMakePath%" "-DCMAKE_BUILD_TYPE=%CMAKE_BUILD_TYPE%" "-DCMAKE_INSTALL_PREFIX=%__CMakeBinDir%" "-DMKL_LIB_PATH=%MKL_LIB_PATH%" -G "Visual Studio %__VSString%" -B. -H%1 endlocal GOTO :DONE diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv index fe46ca26e1..700c55b28a 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv +++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv @@ -59,12 +59,14 @@ Trainers.LogisticRegressionBinaryClassifier Logistic Regression is a method in s Trainers.LogisticRegressionClassifier Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function. Microsoft.ML.Runtime.Learners.LogisticRegression TrainMultiClass Microsoft.ML.Runtime.Learners.MulticlassLogisticRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.NaiveBayesClassifier Train a MultiClassNaiveBayesTrainer. Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer TrainMultiClassNaiveBayesTrainer Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.OnlineGradientDescentRegressor Train a Online gradient descent perceptron. Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer TrainRegression Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput +Trainers.OrdinaryLeastSquaresRegressor Train an OLS regression model. Microsoft.ML.Runtime.HalLearners.OlsLinearRegressionTrainer TrainRegression Microsoft.ML.Runtime.HalLearners.OlsLinearRegressionTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.PcaAnomalyDetector Train an PCA Anomaly model. Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer TrainPcaAnomaly Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+AnomalyDetectionOutput Trainers.PoissonRegressor Train an Poisson regression model. Microsoft.ML.Runtime.Learners.PoissonRegression TrainRegression Microsoft.ML.Runtime.Learners.PoissonRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.StochasticDualCoordinateAscentBinaryClassifier Train an SDCA binary model. Microsoft.ML.Runtime.Learners.Sdca TrainBinary Microsoft.ML.Runtime.Learners.LinearClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.StochasticDualCoordinateAscentClassifier The SDCA linear multi-class classification trainer. Microsoft.ML.Runtime.Learners.Sdca TrainMultiClass Microsoft.ML.Runtime.Learners.SdcaMultiClassTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.StochasticDualCoordinateAscentRegressor The SDCA linear regression trainer. Microsoft.ML.Runtime.Learners.Sdca TrainRegression Microsoft.ML.Runtime.Learners.SdcaRegressionTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.StochasticGradientDescentBinaryClassifier Train an Hogwild SGD binary model. Microsoft.ML.Runtime.Learners.StochasticGradientDescentClassificationTrainer TrainBinary Microsoft.ML.Runtime.Learners.StochasticGradientDescentClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput +Trainers.SymSgdBinaryClassifier Train a symbolic SGD. Microsoft.ML.Runtime.SymSgd.SymSgdClassificationTrainer TrainSymSgd Microsoft.ML.Runtime.SymSgd.SymSgdClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Transforms.ApproximateBootstrapSampler Approximate bootstrap sampling. Microsoft.ML.Runtime.Data.BootstrapSample GetSample Microsoft.ML.Runtime.Data.BootstrapSampleTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.BinaryPredictionScoreColumnsRenamer For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class. Microsoft.ML.Runtime.EntryPoints.ScoreModel RenameBinaryPredictionScoreColumns Microsoft.ML.Runtime.EntryPoints.ScoreModel+RenameBinaryPredictionScoreColumnsInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.BinNormalizer The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins. Microsoft.ML.Runtime.Data.Normalize Bin Microsoft.ML.Runtime.Data.NormalizeTransform+BinArguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput @@ -86,7 +88,11 @@ Transforms.FeatureCombiner Combines all the features into one feature column. Mi Transforms.FeatureSelectorByCount Selects the slots for which the count of non-default values is greater than or equal to a threshold. Microsoft.ML.Runtime.EntryPoints.SelectFeatures CountSelect Microsoft.ML.Runtime.Data.CountFeatureSelectionTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.FeatureSelectorByMutualInformation Selects the top k slots across all specified columns ordered by their mutual information with the label column. Microsoft.ML.Runtime.EntryPoints.SelectFeatures MutualInformationSelect Microsoft.ML.Runtime.Data.MutualInformationFeatureSelectionTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.GlobalContrastNormalizer Performs a global contrast normalization on input values: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation. Microsoft.ML.Runtime.Data.LpNormalization GcNormalize Microsoft.ML.Runtime.Data.LpNormNormalizerTransform+GcnArguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput -Transforms.HashConverter Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform. Microsoft.ML.Runtime.Data.HashJoin Apply Microsoft.ML.Runtime.Data.HashJoinTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.HashConverter Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. Microsoft.ML.Runtime.Data.HashJoin Apply Microsoft.ML.Runtime.Data.HashJoinTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.ImageGrayscale Convert image into grayscale. Microsoft.ML.Runtime.ImageAnalytics.EntryPoints.ImageAnalytics ImageGrayscale Microsoft.ML.Runtime.ImageAnalytics.ImageGrayscaleTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.ImageLoader Load images from files. Microsoft.ML.Runtime.ImageAnalytics.EntryPoints.ImageAnalytics ImageLoader Microsoft.ML.Runtime.ImageAnalytics.ImageLoaderTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.ImagePixelExtractor Extract color plane(s) from an image. Options include scaling, offset and conversion to floating point. Microsoft.ML.Runtime.ImageAnalytics.EntryPoints.ImageAnalytics ImagePixelExtractor Microsoft.ML.Runtime.ImageAnalytics.ImagePixelExtractorTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.ImageResizer Scales an image to specified dimensions using one of the three scale types: isotropic with padding, isotropic with cropping or anisotropic. In case of isotropic padding, transparent color is used to pad resulting image. Microsoft.ML.Runtime.ImageAnalytics.EntryPoints.ImageAnalytics ImageResizer Microsoft.ML.Runtime.ImageAnalytics.ImageResizerTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.KeyToTextConverter KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata. Microsoft.ML.Runtime.Data.Categorical KeyToText Microsoft.ML.Runtime.Data.KeyToValueTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LabelColumnKeyBooleanConverter Transforms the label to either key or bool (if needed) to make it suitable for classification. Microsoft.ML.Runtime.EntryPoints.FeatureCombiner PrepareClassificationLabel Microsoft.ML.Runtime.EntryPoints.FeatureCombiner+ClassificationLabelInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LabelIndicator Label remapper used by OVA Microsoft.ML.Runtime.Data.LabelIndicatorTransform LabelIndicator Microsoft.ML.Runtime.Data.LabelIndicatorTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput @@ -123,4 +129,6 @@ Transforms.TextToKeyConverter Converts input values (words, numbers, etc.) to in Transforms.TrainTestDatasetSplitter Split the dataset into train and test sets Microsoft.ML.Runtime.EntryPoints.TrainTestSplit Split Microsoft.ML.Runtime.EntryPoints.TrainTestSplit+Input Microsoft.ML.Runtime.EntryPoints.TrainTestSplit+Output Transforms.TreeLeafFeaturizer Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices. Microsoft.ML.Runtime.Data.TreeFeaturize Featurizer Microsoft.ML.Runtime.Data.TreeEnsembleFeaturizerTransform+ArgumentsForEntryPoint Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.TwoHeterogeneousModelCombiner Combines a TransformModel and a PredictorModel into a single PredictorModel. Microsoft.ML.Runtime.EntryPoints.ModelOperations CombineTwoModels Microsoft.ML.Runtime.EntryPoints.ModelOperations+SimplePredictorModelInput Microsoft.ML.Runtime.EntryPoints.ModelOperations+PredictorModelOutput +Transforms.VectorToImage Converts vector array into image type. Microsoft.ML.Runtime.ImageAnalytics.EntryPoints.ImageAnalytics VectorToImage Microsoft.ML.Runtime.ImageAnalytics.VectorToImageTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.WordEmbeddings Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence vectors using a pre-trained model Microsoft.ML.Runtime.Transforms.TextAnalytics WordEmbeddings Microsoft.ML.Runtime.Data.WordEmbeddingsTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.WordTokenizer The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed. Microsoft.ML.Runtime.Transforms.TextAnalytics DelimitedTokenizeTransform Microsoft.ML.Runtime.Data.DelimitedTokenizeTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 36bdd0f62a..1b68fee796 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -2417,10 +2417,9 @@ "Name": "Onnx", "Type": "String", "Desc": "The path to write the output ONNX to.", - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Json", @@ -14050,6 +14049,149 @@ "ITrainerOutput" ] }, + { + "Name": "Trainers.OrdinaryLeastSquaresRegressor", + "Desc": "Train an OLS regression model.", + "FriendlyName": "Ordinary Least Squares (Regression)", + "ShortName": "ols", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "L2Weight", + "Type": "Float", + "Desc": "L2 regularization weight", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1E-06, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1E-06, + 0.1, + 1.0 + ] + } + }, + { + "Name": "PerParameterSignificance", + "Type": "Bool", + "Desc": "Whether to calculate per parameter significance statistics", + "Aliases": [ + "sig" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, { "Name": "Trainers.PcaAnomalyDetector", "Desc": "Train an PCA Anomaly model.", @@ -15648,83 +15790,330 @@ ] }, { - "Name": "Transforms.ApproximateBootstrapSampler", - "Desc": "Approximate bootstrap sampling.", - "FriendlyName": "Bootstrap Sample Transform", - "ShortName": "BootstrapSample", + "Name": "Trainers.SymSgdBinaryClassifier", + "Desc": "Train a symbolic SGD.", + "FriendlyName": "Symbolic SGD (binary)", + "ShortName": "SymSGD", "Inputs": [ { - "Name": "Data", + "Name": "TrainingData", "Type": "DataView", - "Desc": "Input dataset", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Complement", - "Type": "Bool", - "Desc": "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "comp" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false + "Default": "Features" }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed. If unspecified random state will be instead derived from the environment.", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" }, { - "Name": "ShuffleInput", - "Type": "Bool", - "Desc": "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", "Aliases": [ - "si" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": true + "Default": "Auto" }, { - "Name": "PoolSize", - "Type": "Int", - "Desc": "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "pool" + "cache" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": 1000 - } - ], - "Outputs": [ + "Default": "Auto" + }, { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Name": "NumberOfIterations", + "Type": "Int", + "Desc": "Number of passes over the data.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 50, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 5, + 10, + 20, + 30, + 40, + 50 + ] + } }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] + "Name": "LearningRate", + "Type": "Float", + "Desc": "Learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 51.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10.0, + 1.0, + 0.1, + 0.01, + 0.001 + ] + } + }, + { + "Name": "L2Regularization", + "Type": "Float", + "Desc": "L2 regularization", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 52.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-05, + 1E-05, + 1E-06, + 1E-07 + ] + } + }, + { + "Name": "NumberOfThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Determinism not guaranteed. Multi-threading is not supported currently.", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Tolerance", + "Type": "Float", + "Desc": "Tolerance for difference in average loss in consecutive passes.", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0001 + }, + { + "Name": "UpdateFrequency", + "Type": "Int", + "Desc": "The number of iterations each thread learns a local model until combining it with the global model. Low value means more updated global model and high value means less cache traffic.", + "Aliases": [ + "freq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 5, + 20 + ] + } + }, + { + "Name": "MemorySize", + "Type": "Int", + "Desc": "The acceleration memory budget in MB", + "Aliases": [ + "accelMemBudget" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1024 + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Transforms.ApproximateBootstrapSampler", + "Desc": "Approximate bootstrap sampling.", + "FriendlyName": "Bootstrap Sample Transform", + "ShortName": "BootstrapSample", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", + "Aliases": [ + "comp" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed. If unspecified random state will be instead derived from the environment.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ShuffleInput", + "Type": "Bool", + "Desc": "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", + "Aliases": [ + "si" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "PoolSize", + "Type": "Int", + "Desc": "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", + "Aliases": [ + "pool" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] }, { "Name": "Transforms.BinaryPredictionScoreColumnsRenamer", @@ -17673,7 +18062,7 @@ }, { "Name": "Transforms.HashConverter", - "Desc": "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform.", + "Desc": "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. ", "FriendlyName": "Hash Join Transform", "ShortName": "HashJoin", "Inputs": [ @@ -17841,10 +18230,10 @@ ] }, { - "Name": "Transforms.KeyToTextConverter", - "Desc": "KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.", - "FriendlyName": "Key To Value Transform", - "ShortName": null, + "Name": "Transforms.ImageGrayscale", + "Desc": "Convert image into grayscale.", + "FriendlyName": "Image Greyscale Transform", + "ShortName": "ImageGrayscaleTransform", "Inputs": [ { "Name": "Column", @@ -17917,11 +18306,53 @@ ] }, { - "Name": "Transforms.LabelColumnKeyBooleanConverter", - "Desc": "Transforms the label to either key or bool (if needed) to make it suitable for classification.", - "FriendlyName": "Prepare Classification Label", - "ShortName": null, + "Name": "Transforms.ImageLoader", + "Desc": "Load images from files.", + "FriendlyName": "Image Loader Transform", + "ShortName": "ImageLoaderTransform", "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, { "Name": "Data", "Type": "DataView", @@ -17931,21 +18362,16 @@ "IsNullable": false }, { - "Name": "LabelColumn", + "Name": "ImageFolder", "Type": "String", - "Desc": "The label column", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false - }, - { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Convert the key values to text", + "Desc": "Folder where to search for images", + "Aliases": [ + "folder" + ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": null } ], "Outputs": [ @@ -17968,10 +18394,10 @@ ] }, { - "Name": "Transforms.LabelIndicator", - "Desc": "Label remapper used by OVA", - "FriendlyName": "LabelIndicator", - "ShortName": "LabelIndictator", + "Name": "Transforms.ImagePixelExtractor", + "Desc": "Extract color plane(s) from an image. Options include scaling, offset and conversion to floating point.", + "FriendlyName": "Image Pixel Extractor Transform", + "ShortName": "ImagePixelExtractor", "Inputs": [ { "Name": "Column", @@ -17981,17 +18407,95 @@ "Kind": "Struct", "Fields": [ { - "Name": "ClassIndex", - "Type": "Int", - "Desc": "The positive example class for binary classification.", + "Name": "UseAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", "Aliases": [ - "index" + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "UseBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "InterleaveArgb", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in ARGB order", + "Aliases": [ + "interleave" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Convert", + "Type": "Bool", + "Desc": "Whether to convert to floating point", + "Aliases": [ + "conv" ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, { "Name": "Name", "Type": "String", @@ -18023,10 +18527,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -18037,58 +18540,94 @@ "IsNullable": false }, { - "Name": "ClassIndex", - "Type": "Int", - "Desc": "Label of the positive class.", + "Name": "UseAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", "Aliases": [ - "index" + "alpha" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 - } - ], - "Outputs": [ + "Default": false + }, { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Name": "UseRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.LabelToFloatConverter", - "Desc": "Transforms the label to float to make it suitable for regression.", - "FriendlyName": "Prepare Regression Label", - "ShortName": null, - "Inputs": [ + "Name": "UseGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "UseBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "The label column", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false + "Name": "InterleaveArgb", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in ARGB order", + "Aliases": [ + "interleave" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Convert", + "Type": "Bool", + "Desc": "Whether to convert to floating point", + "Aliases": [ + "conv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null } ], "Outputs": [ @@ -18111,19 +18650,11 @@ ] }, { - "Name": "Transforms.LightLda", - "Desc": "The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation.", - "FriendlyName": "Latent Dirichlet Allocation Transform", - "ShortName": "LightLda", + "Name": "Transforms.ImageResizer", + "Desc": "Scales an image to specified dimensions using one of the three scale types: isotropic with padding, isotropic with cropping or anisotropic. In case of isotropic padding, transparent color is used to pad resulting image.", + "FriendlyName": "Image Resizer Transform", + "ShortName": "ImageScalerTransform", "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, { "Name": "Column", "Type": { @@ -18132,47 +18663,62 @@ "Kind": "Struct", "Fields": [ { - "Name": "NumTopic", + "Name": "ImageWidth", "Type": "Int", - "Desc": "The number of topics in the LDA", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "AlphaSum", - "Type": "Float", - "Desc": "Dirichlet prior on document-topic vectors", + "Desc": "Width of the resized image", + "Aliases": [ + "width" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "Beta", - "Type": "Float", - "Desc": "Dirichlet prior on vocab-topic vectors", + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the resized image", + "Aliases": [ + "height" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "Mhstep", - "Type": "Int", - "Desc": "Number of Metropolis Hasting step", + "Name": "Resizing", + "Type": { + "Kind": "Enum", + "Values": [ + "IsoPad", + "IsoCrop" + ] + }, + "Desc": "Resizing method", + "Aliases": [ + "scale" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "NumIterations", - "Type": "Int", - "Desc": "Number of iterations", + "Name": "CropAnchor", + "Type": { + "Kind": "Enum", + "Values": [ + "Right", + "Left", + "Top", + "Bottom", + "Center" + ] + }, + "Desc": "Anchor for cropping", "Aliases": [ - "iter" + "anchor" ], "Required": false, "SortOrder": 150.0, @@ -18180,297 +18726,110 @@ "Default": null }, { - "Name": "LikelihoodInterval", - "Type": "Int", - "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", "Aliases": [ - "llInterval" + "name" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, + "IsNullable": false, "Default": null }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of training threads", + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", "Aliases": [ - "t" + "src" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "NumMaxDocToken", - "Type": "Int", - "Desc": "The threshold of maximum count of tokens per doc", - "Aliases": [ - "maxNumToken" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "NumSummaryTermPerTopic", - "Type": "Int", - "Desc": "The number of words to summarize the topic", - "Aliases": [ - "ns" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "NumBurninIterations", - "Type": "Int", - "Desc": "The number of burn-in iterations", - "Aliases": [ - "burninIter" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": 10 - }, - { - "Name": "ResetRandomGenerator", - "Type": "Bool", - "Desc": "Reset the random number generator for each document", - "Aliases": [ - "reset" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": false, "Default": null } ] } }, - "Desc": "New column definition(s) (optional form: name:srcs)", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], "Required": true, - "SortOrder": 49.0, + "SortOrder": 1.0, "IsNullable": false }, { - "Name": "NumTopic", - "Type": "Int", - "Desc": "The number of topics in the LDA", - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": 100, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 40, - 100, - 200 - ] - } + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "NumMaxDocToken", + "Name": "ImageWidth", "Type": "Int", - "Desc": "The threshold of maximum count of tokens per doc", + "Desc": "Resized width of the image", "Aliases": [ - "maxNumToken" + "width" ], - "Required": false, - "SortOrder": 50.0, + "Required": true, + "SortOrder": 150.0, "IsNullable": false, - "Default": 512 + "Default": 0 }, { - "Name": "NumThreads", + "Name": "ImageHeight", "Type": "Int", - "Desc": "The number of training threads. Default value depends on number of logical processors.", + "Desc": "Resized height of the image", "Aliases": [ - "t" + "height" ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "AlphaSum", - "Type": "Float", - "Desc": "Dirichlet prior on document-topic vectors", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 100.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1, - 10, - 100, - 200 - ] - } - }, - { - "Name": "Beta", - "Type": "Float", - "Desc": "Dirichlet prior on vocab-topic vectors", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.01, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.01, - 0.015, - 0.07, - 0.02 - ] - } - }, - { - "Name": "Mhstep", - "Type": "Int", - "Desc": "Number of Metropolis Hasting step", - "Required": false, + "Required": true, "SortOrder": 150.0, "IsNullable": false, - "Default": 4, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 2, - 4, - 8, - 16 - ] - } + "Default": 0 }, { - "Name": "NumIterations", - "Type": "Int", - "Desc": "Number of iterations", - "Aliases": [ - "iter" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 200, - "SweepRange": { - "RangeType": "Discrete", + "Name": "Resizing", + "Type": { + "Kind": "Enum", "Values": [ - 100, - 200, - 300, - 400 + "IsoPad", + "IsoCrop" ] - } - }, - { - "Name": "LikelihoodInterval", - "Type": "Int", - "Desc": "Compute log likelihood over local dataset on this iteration interval", - "Aliases": [ - "llInterval" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 5 - }, - { - "Name": "NumSummaryTermPerTopic", - "Type": "Int", - "Desc": "The number of words to summarize the topic", + }, + "Desc": "Resizing method", "Aliases": [ - "ns" + "scale" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 10 + "Default": "IsoCrop" }, { - "Name": "NumBurninIterations", - "Type": "Int", - "Desc": "The number of burn-in iterations", - "Aliases": [ - "burninIter" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 10, - "SweepRange": { - "RangeType": "Discrete", + "Name": "CropAnchor", + "Type": { + "Kind": "Enum", "Values": [ - 10, - 20, - 30, - 40 + "Right", + "Left", + "Top", + "Bottom", + "Center" ] - } - }, - { - "Name": "ResetRandomGenerator", - "Type": "Bool", - "Desc": "Reset the random number generator for each document", - "Aliases": [ - "reset" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "OutputTopicWordSummary", - "Type": "Bool", - "Desc": "Whether to output the topic-word summary in text format", + }, + "Desc": "Anchor for cropping", "Aliases": [ - "summary" + "anchor" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": "Center" } ], "Outputs": [ @@ -18493,10 +18852,10 @@ ] }, { - "Name": "Transforms.LogMeanVarianceNormalizer", - "Desc": "Normalizes the data based on the computed mean and variance of the logarithm of the data.", - "FriendlyName": "LogMeanVar Normalizer", - "ShortName": "LogMeanVar", + "Name": "Transforms.KeyToTextConverter", + "Desc": "KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.", + "FriendlyName": "Key To Value Transform", + "ShortName": null, "Inputs": [ { "Name": "Column", @@ -18505,18 +18864,6 @@ "ItemType": { "Kind": "Struct", "Fields": [ - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, { "Name": "Name", "Type": "String", @@ -18548,10 +18895,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -18560,30 +18906,57 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "UseCdf", - "Type": "Bool", - "Desc": "Whether to use CDF as the output", - "Aliases": [ - "cdf" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LabelColumnKeyBooleanConverter", + "Desc": "Transforms the label to either key or bool (if needed) to make it suitable for classification.", + "FriendlyName": "Prepare Classification Label", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Convert the key values to text", "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 1000000000 + "Default": true } ], "Outputs": [ @@ -18606,10 +18979,10 @@ ] }, { - "Name": "Transforms.LpNormalizer", - "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm.", - "FriendlyName": "Lp-Norm Normalizer", - "ShortName": "lpnorm", + "Name": "Transforms.LabelIndicator", + "Desc": "Label remapper used by OVA", + "FriendlyName": "LabelIndicator", + "ShortName": "LabelIndictator", "Inputs": [ { "Name": "Column", @@ -18619,30 +18992,13 @@ "Kind": "Struct", "Fields": [ { - "Name": "NormKind", - "Type": { - "Kind": "Enum", - "Values": [ - "L2Norm", - "StdDev", - "L1Norm", - "LInf" - ] - }, - "Desc": "The norm to use to normalize each sample", + "Name": "ClassIndex", + "Type": "Int", + "Desc": "The positive example class for binary classification.", "Aliases": [ - "norm" + "index" ], "Required": false, - "SortOrder": 1.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "SubMean", - "Type": "Bool", - "Desc": "Subtract mean from each value before normalizing", - "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null @@ -18678,29 +19034,10 @@ "Aliases": [ "col" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "NormKind", - "Type": { - "Kind": "Enum", - "Values": [ - "L2Norm", - "StdDev", - "L1Norm", - "LInf" - ] - }, - "Desc": "The norm to use to normalize each sample", - "Aliases": [ - "norm" - ], "Required": false, "SortOrder": 1.0, "IsNullable": false, - "Default": "L2Norm" + "Default": null }, { "Name": "Data", @@ -18711,13 +19048,16 @@ "IsNullable": false }, { - "Name": "SubMean", - "Type": "Bool", - "Desc": "Subtract mean from each value before normalizing", + "Name": "ClassIndex", + "Type": "Int", + "Desc": "Label of the positive class.", + "Aliases": [ + "index" + ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0 } ], "Outputs": [ @@ -18740,26 +19080,23 @@ ] }, { - "Name": "Transforms.ManyHeterogeneousModelCombiner", - "Desc": "Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.", - "FriendlyName": null, + "Name": "Transforms.LabelToFloatConverter", + "Desc": "Transforms the label to float to make it suitable for regression.", + "FriendlyName": "Prepare Regression Label", "ShortName": null, "Inputs": [ { - "Name": "TransformModels", - "Type": { - "Kind": "Array", - "ItemType": "TransformModel" - }, - "Desc": "Transform model", + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model", + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", "Required": true, "SortOrder": 2.0, "IsNullable": false @@ -18767,18 +19104,37 @@ ], "Outputs": [ { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model" + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" ] }, { - "Name": "Transforms.MeanVarianceNormalizer", - "Desc": "Normalizes the data based on the computed mean and variance of the data.", - "FriendlyName": "MeanVar Normalizer", - "ShortName": "MeanVar", + "Name": "Transforms.LightLda", + "Desc": "The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation.", + "FriendlyName": "Latent Dirichlet Allocation Transform", + "ShortName": "LightLda", "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, { "Name": "Column", "Type": { @@ -18787,23 +19143,47 @@ "Kind": "Struct", "Fields": [ { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics in the LDA", "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "MaxTrainingExamples", + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Mhstep", "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", "Aliases": [ - "maxtrain" + "iter" ], "Required": false, "SortOrder": 150.0, @@ -18811,123 +19191,23 @@ "Default": null }, { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", "Aliases": [ - "name" + "llInterval" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads", "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "UseCdf", - "Type": "Bool", - "Desc": "Whether to use CDF as the output", - "Aliases": [ - "cdf" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1000000000 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.MinMaxNormalizer", - "Desc": "Normalizes the data based on the observed minimum and maximum values of the data.", - "FriendlyName": "Min-Max Normalizer", - "ShortName": "MinMax", - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" + "t" ], "Required": false, "SortOrder": 150.0, @@ -18935,11 +19215,11 @@ "Default": null }, { - "Name": "MaxTrainingExamples", + "Name": "NumMaxDocToken", "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Desc": "The threshold of maximum count of tokens per doc", "Aliases": [ - "maxtrain" + "maxNumToken" ], "Required": false, "SortOrder": 150.0, @@ -18947,140 +19227,35 @@ "Default": null }, { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", "Aliases": [ - "src" + "ns" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1000000000 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.MissingValueHandler", - "Desc": "Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric.", - "FriendlyName": "NA Handle Transform", - "ShortName": "NAHandle", - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Kind", - "Type": { - "Kind": "Enum", - "Values": [ - "DefaultValue", - "Mean", - "Minimum", - "Maximum" - ] - }, - "Desc": "The replacement method to utilize", - "Required": false, - "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "ImputeBySlot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", "Aliases": [ - "slot" + "burninIter" ], "Required": false, "SortOrder": 150.0, "IsNullable": true, - "Default": null + "Default": 10 }, { - "Name": "ConcatIndicator", + "Name": "ResetRandomGenerator", "Type": "Bool", - "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Desc": "Reset the random number generator for each document", "Aliases": [ - "ind" + "reset" ], "Required": false, "SortOrder": 150.0, @@ -19114,65 +19289,199 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:rep:src)", + "Desc": "New column definition(s) (optional form: name:srcs)", "Aliases": [ "col" ], "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, + "SortOrder": 49.0, "IsNullable": false }, { - "Name": "ReplaceWith", - "Type": { - "Kind": "Enum", - "Values": [ - "DefaultValue", - "Mean", - "Minimum", - "Maximum" - ] - }, - "Desc": "The replacement method to utilize", + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics in the LDA", + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 40, + 100, + 200 + ] + } + }, + { + "Name": "NumMaxDocToken", + "Type": "Int", + "Desc": "The threshold of maximum count of tokens per doc", "Aliases": [ - "kind" + "maxNumToken" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": "Def" + "Default": 512 }, { - "Name": "ImputeBySlot", + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads. Default value depends on number of logical processors.", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 100, + 200 + ] + } + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.015, + 0.07, + 0.02 + ] + } + }, + { + "Name": "Mhstep", + "Type": "Int", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 4, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 2, + 4, + 8, + 16 + ] + } + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 200, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 100, + 200, + 300, + 400 + ] + } + }, + { + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Aliases": [ + "llInterval" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", + "Aliases": [ + "ns" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", + "Aliases": [ + "burninIter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 30, + 40 + ] + } + }, + { + "Name": "ResetRandomGenerator", "Type": "Bool", - "Desc": "Whether to impute values by slot", + "Desc": "Reset the random number generator for each document", "Aliases": [ - "slot" + "reset" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": false }, { - "Name": "Concat", + "Name": "OutputTopicWordSummary", "Type": "Bool", - "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Desc": "Whether to output the topic-word summary in text format", "Aliases": [ - "ind" + "summary" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": false } ], "Outputs": [ @@ -19195,10 +19504,10 @@ ] }, { - "Name": "Transforms.MissingValueIndicator", - "Desc": "Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.", - "FriendlyName": "NA Indicator Transform", - "ShortName": "NAInd", + "Name": "Transforms.LogMeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the logarithm of the data.", + "FriendlyName": "LogMeanVar Normalizer", + "ShortName": "LogMeanVar", "Inputs": [ { "Name": "Column", @@ -19207,6 +19516,18 @@ "ItemType": { "Kind": "Struct", "Fields": [ + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, { "Name": "Name", "Type": "String", @@ -19238,9 +19559,10 @@ "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { "Name": "Data", @@ -19249,6 +19571,30 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false + }, + { + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", + "Aliases": [ + "cdf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 } ], "Outputs": [ @@ -19271,10 +19617,10 @@ ] }, { - "Name": "Transforms.MissingValuesDropper", - "Desc": "Removes NAs from vector columns.", - "FriendlyName": "NA Drop Transform", - "ShortName": "NADrop", + "Name": "Transforms.LpNormalizer", + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm.", + "FriendlyName": "Lp-Norm Normalizer", + "ShortName": "lpnorm", "Inputs": [ { "Name": "Column", @@ -19284,20 +19630,49 @@ "Kind": "Struct", "Fields": [ { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", + "Name": "NormKind", + "Type": { + "Kind": "Enum", + "Values": [ + "L2Norm", + "StdDev", + "L1Norm", + "LInf" + ] + }, + "Desc": "The norm to use to normalize each sample", "Aliases": [ - "name" + "norm" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": false, + "SortOrder": 1.0, + "IsNullable": true, "Default": null }, { - "Name": "Source", - "Type": "String", + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", "Desc": "Name of the source column", "Aliases": [ "src" @@ -19310,7 +19685,7 @@ ] } }, - "Desc": "Columns to drop the NAs for", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], @@ -19318,6 +19693,26 @@ "SortOrder": 1.0, "IsNullable": false }, + { + "Name": "NormKind", + "Type": { + "Kind": "Enum", + "Values": [ + "L2Norm", + "StdDev", + "L1Norm", + "LInf" + ] + }, + "Desc": "The norm to use to normalize each sample", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": "L2Norm" + }, { "Name": "Data", "Type": "DataView", @@ -19325,6 +19720,15 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": false } ], "Outputs": [ @@ -19347,67 +19751,44 @@ ] }, { - "Name": "Transforms.MissingValuesRowDropper", - "Desc": "Filters out rows that contain missing values.", - "FriendlyName": "NA Filter", - "ShortName": "NAFilter", + "Name": "Transforms.ManyHeterogeneousModelCombiner", + "Desc": "Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, + "ShortName": null, "Inputs": [ { - "Name": "Column", + "Name": "TransformModels", "Type": { "Kind": "Array", - "ItemType": "String" + "ItemType": "TransformModel" }, - "Desc": "Column", - "Aliases": [ - "col" - ], + "Desc": "Transform model", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", "Required": true, - "SortOrder": 1.0, + "SortOrder": 2.0, "IsNullable": false - }, - { - "Name": "Complement", - "Type": "Bool", - "Desc": "If true, keep only rows that contain NA values, and filter the rest.", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false } ], "Outputs": [ { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" ] }, { - "Name": "Transforms.MissingValueSubstitutor", - "Desc": "Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only).", - "FriendlyName": "NA Replace Transform", - "ShortName": "NARep", + "Name": "Transforms.MeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the data.", + "FriendlyName": "MeanVar Normalizer", + "ShortName": "MeanVar", "Inputs": [ { "Name": "Column", @@ -19417,39 +19798,24 @@ "Kind": "Struct", "Fields": [ { - "Name": "ReplacementString", - "Type": "String", - "Desc": "Replacement value for NAs (uses default value if not given)", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "rep" + "zero" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Kind", - "Type": { - "Kind": "Enum", - "Values": [ - "DefaultValue", - "Mean", - "Minimum", - "Maximum", - "SpecifiedValue" - ] - }, - "Desc": "The replacement method to utilize", - "Required": false, - "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "Slot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, @@ -19482,7 +19848,7 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:rep:src)", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], @@ -19499,37 +19865,40 @@ "IsNullable": false }, { - "Name": "ReplacementKind", - "Type": { - "Kind": "Enum", - "Values": [ - "DefaultValue", - "Mean", - "Minimum", - "Maximum", - "SpecifiedValue" - ] - }, - "Desc": "The replacement method to utilize", + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", "Aliases": [ - "kind" + "cdf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "Default" + "Default": false }, { - "Name": "ImputeBySlot", + "Name": "FixZero", "Type": "Bool", - "Desc": "Whether to impute values by slot", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "slot" + "zero" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 } ], "Outputs": [ @@ -19552,37 +19921,10 @@ ] }, { - "Name": "Transforms.ModelCombiner", - "Desc": "Combines a sequence of TransformModels into a single model", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "Models", - "Type": { - "Kind": "Array", - "ItemType": "TransformModel" - }, - "Desc": "Input models", - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - } - ], - "Outputs": [ - { - "Name": "OutputModel", - "Type": "TransformModel", - "Desc": "Combined model" - } - ] - }, - { - "Name": "Transforms.NGramTranslator", - "Desc": "Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag.", - "FriendlyName": "NGram Transform", - "ShortName": "NgramTransform", + "Name": "Transforms.MinMaxNormalizer", + "Desc": "Normalizes the data based on the observed minimum and maximum values of the data.", + "FriendlyName": "Min-Max Normalizer", + "ShortName": "MinMax", "Inputs": [ { "Name": "Column", @@ -19592,11 +19934,11 @@ "Kind": "Struct", "Fields": [ { - "Name": "NgramLength", - "Type": "Int", - "Desc": "Maximum ngram length", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "ngram" + "zero" ], "Required": false, "SortOrder": 150.0, @@ -19604,11 +19946,11 @@ "Default": null }, { - "Name": "AllLengths", - "Type": "Bool", - "Desc": "Whether to include all ngram lengths up to NgramLength or only NgramLength", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "all" + "maxtrain" ], "Required": false, "SortOrder": 150.0, @@ -19616,58 +19958,15 @@ "Default": null }, { - "Name": "SkipLength", - "Type": "Int", - "Desc": "Maximum number of tokens to skip when constructing an ngram", + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", "Aliases": [ - "skips" + "name" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxNumTerms", - "Type": { - "Kind": "Array", - "ItemType": "Int" - }, - "Desc": "Maximum number of ngrams to store in the dictionary", - "Aliases": [ - "max" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Weighting", - "Type": { - "Kind": "Enum", - "Values": [ - "Tf", - "Idf", - "TfIdf" - ] - }, - "Desc": "Statistical measure used to evaluate how important a word is to a document in a corpus", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": false, "Default": null }, { @@ -19689,10 +19988,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -19703,23 +20001,11 @@ "IsNullable": false }, { - "Name": "NgramLength", - "Type": "Int", - "Desc": "Maximum ngram length", - "Aliases": [ - "ngram" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 2 - }, - { - "Name": "AllLengths", + "Name": "FixZero", "Type": "Bool", - "Desc": "Whether to store all ngram lengths up to ngramLength, or only ngramLength", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "all" + "zero" ], "Required": false, "SortOrder": 150.0, @@ -19727,131 +20013,16 @@ "Default": true }, { - "Name": "SkipLength", + "Name": "MaxTrainingExamples", "Type": "Int", - "Desc": "Maximum number of tokens to skip when constructing an ngram", - "Aliases": [ - "skips" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 - }, - { - "Name": "MaxNumTerms", - "Type": { - "Kind": "Array", - "ItemType": "Int" - }, - "Desc": "Maximum number of ngrams to store in the dictionary", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "max" + "maxtrain" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": [ - 10000000 - ] - }, - { - "Name": "Weighting", - "Type": { - "Kind": "Enum", - "Values": [ - "Tf", - "Idf", - "TfIdf" - ] - }, - "Desc": "The weighting criteria", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": "Tf" - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.NoOperation", - "Desc": "Does nothing.", - "FriendlyName": "No Op", - "ShortName": "Nop", - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.OptionalColumnCreator", - "Desc": "If the source column does not exist after deserialization, create a column with the right type and default values.", - "FriendlyName": "Optional Column Transform", - "ShortName": "optional", - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "New column definition(s)", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Default": 1000000000 } ], "Outputs": [ @@ -19874,10 +20045,10 @@ ] }, { - "Name": "Transforms.PcaCalculator", - "Desc": "PCA is a dimensionality-reduction transform which computes the projection of a numeric vector onto a low-rank subspace.", - "FriendlyName": "Principal Component Analysis Transform", - "ShortName": "Pca", + "Name": "Transforms.MissingValueHandler", + "Desc": "Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric.", + "FriendlyName": "NA Handle Transform", + "ShortName": "NAHandle", "Inputs": [ { "Name": "Column", @@ -19887,47 +20058,28 @@ "Kind": "Struct", "Fields": [ { - "Name": "WeightColumn", - "Type": "String", - "Desc": "The name of the weight column", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Rank", - "Type": "Int", - "Desc": "The number of components in the PCA", - "Aliases": [ - "k" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Oversampling", - "Type": "Int", - "Desc": "Oversampling parameter for randomized PCA training", - "Aliases": [ - "over" - ], + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "Center", + "Name": "ImputeBySlot", "Type": "Bool", - "Desc": "If enabled, data is centered to be zero mean", + "Desc": "Whether to impute values by slot", "Aliases": [ - "center" + "slot" ], "Required": false, "SortOrder": 150.0, @@ -19935,11 +20087,11 @@ "Default": null }, { - "Name": "Seed", - "Type": "Int", - "Desc": "The seed for random number generation", + "Name": "ConcatIndicator", + "Type": "Bool", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", "Aliases": [ - "seed" + "ind" ], "Required": false, "SortOrder": 150.0, @@ -19973,7 +20125,7 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:src)", + "Desc": "New column definition(s) (optional form: name:rep:src)", "Aliases": [ "col" ], @@ -19990,58 +20142,48 @@ "IsNullable": false }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "The name of the weight column", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Rank", - "Type": "Int", - "Desc": "The number of components in the PCA", + "Name": "ReplaceWith", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", "Aliases": [ - "k" + "kind" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 20 + "Default": "Def" }, { - "Name": "Oversampling", - "Type": "Int", - "Desc": "Oversampling parameter for randomized PCA training", + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", "Aliases": [ - "over" + "slot" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 20 + "Default": true }, { - "Name": "Center", + "Name": "Concat", "Type": "Bool", - "Desc": "If enabled, data is centered to be zero mean", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Aliases": [ + "ind" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": true - }, - { - "Name": "Seed", - "Type": "Int", - "Desc": "The seed for random number generation", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 } ], "Outputs": [ @@ -20064,25 +20206,59 @@ ] }, { - "Name": "Transforms.PredictedLabelColumnOriginalValueConverter", - "Desc": "Transforms a predicted label column to its original values, unless it is of type bool.", - "FriendlyName": "Convert Predicted Label", - "ShortName": null, + "Name": "Transforms.MissingValueIndicator", + "Desc": "Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.", + "FriendlyName": "NA Indicator Transform", + "ShortName": "NAInd", "Inputs": [ { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "PredictedLabelColumn", - "Type": "String", - "Desc": "The predicted label column", + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, - "SortOrder": 2.0, + "SortOrder": 1.0, "IsNullable": false } ], @@ -20106,10 +20282,10 @@ ] }, { - "Name": "Transforms.RandomNumberGenerator", - "Desc": "Adds a column with a generated number sequence.", - "FriendlyName": "Generate Number Transform", - "ShortName": "Generate", + "Name": "Transforms.MissingValuesDropper", + "Desc": "Removes NAs from vector columns.", + "FriendlyName": "NA Drop Transform", + "ShortName": "NADrop", "Inputs": [ { "Name": "Column", @@ -20131,30 +20307,21 @@ "Default": null }, { - "Name": "UseCounter", - "Type": "Bool", - "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", "Aliases": [ - "cnt" + "src" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, + "IsNullable": false, "Default": null } ] } }, - "Desc": "New column definition(s) (optional form: name:seed)", + "Desc": "Columns to drop the NAs for", "Aliases": [ "col" ], @@ -20169,27 +20336,6 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false - }, - { - "Name": "UseCounter", - "Type": "Bool", - "Desc": "Use an auto-incremented integer starting at zero instead of a random number", - "Aliases": [ - "cnt" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 42 } ], "Outputs": [ @@ -20212,14 +20358,17 @@ ] }, { - "Name": "Transforms.RowRangeFilter", - "Desc": "Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values.", - "FriendlyName": "Range Filter", - "ShortName": "RangeFilter", + "Name": "Transforms.MissingValuesRowDropper", + "Desc": "Filters out rows that contain missing values.", + "FriendlyName": "NA Filter", + "ShortName": "NAFilter", "Inputs": [ { "Name": "Column", - "Type": "String", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, "Desc": "Column", "Aliases": [ "col" @@ -20236,50 +20385,14 @@ "SortOrder": 1.0, "IsNullable": false }, - { - "Name": "Min", - "Type": "Float", - "Desc": "Minimum value (0 to 1 for key types)", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Max", - "Type": "Float", - "Desc": "Maximum value (0 to 1 for key types)", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, { "Name": "Complement", "Type": "Bool", - "Desc": "If true, keep the values that fall outside the range.", + "Desc": "If true, keep only rows that contain NA values, and filter the rest.", "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": false - }, - { - "Name": "IncludeMin", - "Type": "Bool", - "Desc": "If true, include in the range the values that are equal to min.", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "IncludeMax", - "Type": "Bool", - "Desc": "If true, include in the range the values that are equal to max.", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null } ], "Outputs": [ @@ -20302,55 +20415,953 @@ ] }, { - "Name": "Transforms.RowSkipAndTakeFilter", - "Desc": "Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging.", - "FriendlyName": "Skip and Take Filter", - "ShortName": "SkipTake", + "Name": "Transforms.MissingValueSubstitutor", + "Desc": "Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only).", + "FriendlyName": "NA Replace Transform", + "ShortName": "NARep", "Inputs": [ { - "Name": "Skip", - "Type": "Int", - "Desc": "Number of items to skip", - "Aliases": [ - "s" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Take", - "Type": "Int", - "Desc": "Number of items to take", - "Aliases": [ - "t" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ReplacementString", + "Type": "String", + "Desc": "Replacement value for NAs (uses default value if not given)", + "Aliases": [ + "rep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue" + ] + }, + "Desc": "The replacement method to utilize", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Slot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:rep:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ReplacementKind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue" + ] + }, + "Desc": "The replacement method to utilize", + "Aliases": [ + "kind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Default" + }, + { + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ModelCombiner", + "Desc": "Combines a sequence of TransformModels into a single model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "TransformModel" + }, + "Desc": "Input models", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputModel", + "Type": "TransformModel", + "Desc": "Combined model" + } + ] + }, + { + "Name": "Transforms.NGramTranslator", + "Desc": "Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag.", + "FriendlyName": "NGram Transform", + "ShortName": "NgramTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum ngram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to include all ngram lengths up to NgramLength or only NgramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an ngram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of ngrams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "Statistical measure used to evaluate how important a word is to a document in a corpus", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum ngram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to store all ngram lengths up to ngramLength, or only ngramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an ngram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of ngrams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": [ + 10000000 + ] + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "The weighting criteria", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Tf" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.NoOperation", + "Desc": "Does nothing.", + "FriendlyName": "No Op", + "ShortName": "Nop", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.OptionalColumnCreator", + "Desc": "If the source column does not exist after deserialization, create a column with the right type and default values.", + "FriendlyName": "Optional Column Transform", + "ShortName": "optional", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "New column definition(s)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.PcaCalculator", + "Desc": "PCA is a dimensionality-reduction transform which computes the projection of a numeric vector onto a low-rank subspace.", + "FriendlyName": "Principal Component Analysis Transform", + "ShortName": "Pca", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "The name of the weight column", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Aliases": [ + "center" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Aliases": [ + "seed" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "The name of the weight column", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20 + }, + { + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.PredictedLabelColumnOriginalValueConverter", + "Desc": "Transforms a predicted label column to its original values, unless it is of type bool.", + "FriendlyName": "Convert Predicted Label", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictedLabelColumn", + "Type": "String", + "Desc": "The predicted label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RandomNumberGenerator", + "Desc": "Adds a column with a generated number sequence.", + "FriendlyName": "Generate Number Transform", + "ShortName": "Generate", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Aliases": [ + "cnt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:seed)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Aliases": [ + "cnt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 42 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowRangeFilter", + "Desc": "Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values.", + "FriendlyName": "Range Filter", + "ShortName": "RangeFilter", + "Inputs": [ + { + "Name": "Column", + "Type": "String", + "Desc": "Column", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Min", + "Type": "Float", + "Desc": "Minimum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Max", + "Type": "Float", + "Desc": "Maximum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "If true, keep the values that fall outside the range.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "IncludeMin", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to min.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "IncludeMax", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to max.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowSkipAndTakeFilter", + "Desc": "Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging.", + "FriendlyName": "Skip and Take Filter", + "ShortName": "SkipTake", + "Inputs": [ + { + "Name": "Skip", + "Type": "Int", + "Desc": "Number of items to skip", + "Aliases": [ + "s" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Take", + "Type": "Int", + "Desc": "Number of items to take", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } ], "InputKind": [ "ITransformInput" @@ -21029,80 +22040,374 @@ } ] }, - "Desc": "A dictionary of whitelisted terms.", + "Desc": "A dictionary of whitelisted terms.", + "Aliases": [ + "dict" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WordFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for words (WordBag/WordHashBag).", + "Aliases": [ + "wordExtractor" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "CharFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for characters (WordBag/WordHashBag).", + "Aliases": [ + "charExtractor" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "NgramLength": 3, + "AllLengths": false, + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "VectorNormalizer", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "L1", + "L2", + "LInf" + ] + }, + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm.", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 13.0, + "IsNullable": false, + "Default": "L2" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TextToKeyConverter", + "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", + "FriendlyName": "Term Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep per column when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "Occurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", "Aliases": [ - "dict" + "textkv" ], "Required": false, - "SortOrder": 10.0, + "SortOrder": 114.0, "IsNullable": false, - "Default": null + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "WordFeatureExtractor", - "Type": { - "Kind": "Component", - "ComponentKind": "NgramExtractor" - }, - "Desc": "Ngram feature extractor to use for words (WordBag/WordHashBag).", + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TrainTestDatasetSplitter", + "Desc": "Split the dataset into train and test sets", + "FriendlyName": "Dataset Train-Test Split", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Fraction", + "Type": "Float", + "Desc": "Fraction of training data", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0.8 + }, + { + "Name": "StratificationColumn", + "Type": "String", + "Desc": "Stratification column", "Aliases": [ - "wordExtractor" + "strat" ], "Required": false, - "SortOrder": 11.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": { - "Name": "NGram", - "Settings": { - "MaxNumTerms": [ - 10000000 - ] - } - } + "Default": null + } + ], + "Outputs": [ + { + "Name": "TrainData", + "Type": "DataView", + "Desc": "Training data" }, { - "Name": "CharFeatureExtractor", - "Type": { - "Kind": "Component", - "ComponentKind": "NgramExtractor" - }, - "Desc": "Ngram feature extractor to use for characters (WordBag/WordHashBag).", + "Name": "TestData", + "Type": "DataView", + "Desc": "Testing data" + } + ] + }, + { + "Name": "Transforms.TreeLeafFeaturizer", + "Desc": "Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices.", + "FriendlyName": "Tree Ensemble Featurization Transform", + "ShortName": "TreeFeat", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Trainer to use", + "Required": true, + "SortOrder": 10.0, + "IsNullable": false + }, + { + "Name": "Suffix", + "Type": "String", + "Desc": "Output column: The suffix to append to the default column names", "Aliases": [ - "charExtractor" + "ex" ], "Required": false, - "SortOrder": 12.0, + "SortOrder": 101.0, "IsNullable": false, - "Default": { - "Name": "NGram", - "Settings": { - "NgramLength": 3, - "AllLengths": false, - "MaxNumTerms": [ - 10000000 - ] - } - } + "Default": null }, { - "Name": "VectorNormalizer", - "Type": { - "Kind": "Enum", - "Values": [ - "None", - "L1", - "L2", - "LInf" - ] - }, - "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm.", + "Name": "LabelPermutationSeed", + "Type": "Int", + "Desc": "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", "Aliases": [ - "norm" + "lps" ], "Required": false, - "SortOrder": 13.0, + "SortOrder": 102.0, "IsNullable": false, - "Default": "L2" + "Default": 0 } ], "Outputs": [ @@ -21118,6 +22423,7 @@ } ], "InputKind": [ + "IFeaturizerInput", "ITransformInput" ], "OutputKind": [ @@ -21125,10 +22431,41 @@ ] }, { - "Name": "Transforms.TextToKeyConverter", - "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", - "FriendlyName": "Term Transform", + "Name": "Transforms.TwoHeterogeneousModelCombiner", + "Desc": "Combines a TransformModel and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, "ShortName": null, + "Inputs": [ + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "Transform model", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" + } + ] + }, + { + "Name": "Transforms.VectorToImage", + "Desc": "Converts vector array into image type.", + "FriendlyName": "Vector To Image Transform", + "ShortName": "VectorToImageConverter", "Inputs": [ { "Name": "Column", @@ -21138,11 +22475,71 @@ "Kind": "Struct", "Fields": [ { - "Name": "MaxNumTerms", + "Name": "ContainsAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", + "Aliases": [ + "alpha" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ContainsBlue", + "Type": "Bool", + "Desc": "Whether to use blue channel", + "Aliases": [ + "blue" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "InterleaveArgb", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in ARGB order", + "Aliases": [ + "interleave" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImageWidth", "Type": "Int", - "Desc": "Maximum number of terms to keep when auto-training", + "Desc": "Width of the image", "Aliases": [ - "max" + "width" ], "Required": false, "SortOrder": 150.0, @@ -21150,39 +22547,30 @@ "Default": null }, { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the image", + "Aliases": [ + "height" + ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", - "Aliases": [ - "textkv" - ], + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", "Required": false, "SortOrder": 150.0, "IsNullable": true, @@ -21219,10 +22607,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -21233,55 +22620,106 @@ "IsNullable": false }, { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep per column when auto-training", + "Name": "ContainsAlpha", + "Type": "Bool", + "Desc": "Whether to use alpha channel", "Aliases": [ - "max" + "alpha" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000 + "Default": false }, { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", + "Name": "ContainsRed", + "Type": "Bool", + "Desc": "Whether to use red channel", + "Aliases": [ + "red" + ], "Required": false, - "SortOrder": 106.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": true }, { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Name": "ContainsGreen", + "Type": "Bool", + "Desc": "Whether to use green channel", + "Aliases": [ + "green" + ], "Required": false, - "SortOrder": 113.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Occurrence" + "Default": true }, { - "Name": "TextKeyValues", + "Name": "ContainsBlue", "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Desc": "Whether to use blue channel", "Aliases": [ - "textkv" + "blue" ], "Required": false, - "SortOrder": 114.0, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "InterleaveArgb", + "Type": "Bool", + "Desc": "Whether to separate each channel or interleave in ARGB order", + "Aliases": [ + "interleave" + ], + "Required": false, + "SortOrder": 150.0, "IsNullable": false, "Default": false + }, + { + "Name": "ImageWidth", + "Type": "Int", + "Desc": "Width of the image", + "Aliases": [ + "width" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "ImageHeight", + "Type": "Int", + "Desc": "Height of the image", + "Aliases": [ + "height" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "Offset", + "Type": "Float", + "Desc": "Offset (pre-scale)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale factor", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null } ], "Outputs": [ @@ -21304,60 +22742,79 @@ ] }, { - "Name": "Transforms.TrainTestDatasetSplitter", - "Desc": "Split the dataset into train and test sets", - "FriendlyName": "Dataset Train-Test Split", - "ShortName": null, + "Name": "Transforms.WordEmbeddings", + "Desc": "Word Embeddings transform is a text featurizer which converts vectors of text tokens into sentence vectors using a pre-trained model", + "FriendlyName": "Word Embeddings Transform", + "ShortName": "WordEmbeddings", "Inputs": [ { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], "Required": true, - "SortOrder": 1.0, + "SortOrder": 0.0, "IsNullable": false }, { - "Name": "Fraction", - "Type": "Float", - "Desc": "Fraction of training data", - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": 0.8 - }, - { - "Name": "StratificationColumn", - "Type": "String", - "Desc": "Stratification column", + "Name": "ModelKind", + "Type": { + "Kind": "Enum", + "Values": [ + "GloVe50D", + "GloVe100D", + "GloVe200D", + "GloVe300D", + "GloVeTwitter25D", + "GloVeTwitter50D", + "GloVeTwitter100D", + "GloVeTwitter200D", + "FastTextWikipedia300D", + "Sswe" + ] + }, + "Desc": "Pre-trained model used to create the vocabulary", "Aliases": [ - "strat" + "model" ], "Required": false, - "SortOrder": 3.0, - "IsNullable": false, - "Default": null - } - ], - "Outputs": [ - { - "Name": "TrainData", - "Type": "DataView", - "Desc": "Training data" + "SortOrder": 1.0, + "IsNullable": true, + "Default": "Sswe" }, - { - "Name": "TestData", - "Type": "DataView", - "Desc": "Testing data" - } - ] - }, - { - "Name": "Transforms.TreeLeafFeaturizer", - "Desc": "Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices.", - "FriendlyName": "Tree Ensemble Featurization Transform", - "ShortName": "TreeFeat", - "Inputs": [ { "Name": "Data", "Type": "DataView", @@ -21367,36 +22824,16 @@ "IsNullable": false }, { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Trainer to use", - "Required": true, - "SortOrder": 10.0, - "IsNullable": false - }, - { - "Name": "Suffix", + "Name": "CustomLookupTable", "Type": "String", - "Desc": "Output column: The suffix to append to the default column names", + "Desc": "Filename for custom word embedding model", "Aliases": [ - "ex" + "dataFile" ], "Required": false, - "SortOrder": 101.0, + "SortOrder": 2.0, "IsNullable": false, "Default": null - }, - { - "Name": "LabelPermutationSeed", - "Type": "Int", - "Desc": "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", - "Aliases": [ - "lps" - ], - "Required": false, - "SortOrder": 102.0, - "IsNullable": false, - "Default": 0 } ], "Outputs": [ @@ -21412,44 +22849,12 @@ } ], "InputKind": [ - "IFeaturizerInput", "ITransformInput" ], "OutputKind": [ "ITransformOutput" ] }, - { - "Name": "Transforms.TwoHeterogeneousModelCombiner", - "Desc": "Combines a TransformModel and a PredictorModel into a single PredictorModel.", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "TransformModel", - "Type": "TransformModel", - "Desc": "Transform model", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model" - } - ] - }, { "Name": "Transforms.WordTokenizer", "Desc": "The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed.", diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt new file mode 100644 index 0000000000..8e81e97339 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-out.txt @@ -0,0 +1,33 @@ +maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 2409 examples +Coefficient of determination R2 = 0.291173667189042, or 0.287920813763543 (adjusted) +Not training a calibrator because it is not needed. +Not adding a normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.280280855195625, or 0.277084686203761 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.586798 +L2(avg): 0.573048 +RMS(avg): 0.756999 +Loss-fn(avg): 0.573048 +R Squared: 0.263841 +L1(avg): 0.587999 +L2(avg): 0.571859 +RMS(avg): 0.756214 +Loss-fn(avg): 0.571859 +R Squared: 0.276072 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.587398 (0.0006) +L2(avg): 0.572454 (0.0006) +RMS(avg): 0.756606 (0.0004) +Loss-fn(avg): 0.572454 (0.0006) +R Squared: 0.269956 (0.0061) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt new file mode 100644 index 0000000000..e3768ce894 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.587398 0.572454 0.756606 0.572454 0.269956 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt new file mode 100644 index 0000000000..c49fe65f22 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.72627258 0.2737274169921875 0.074926698813214898 +6 6 5.47047424 0.5295257568359375 0.28039752715267241 +8 6 5.164154 0.835845947265625 0.69863844756036997 +9 6 5.711914 0.2880859375 0.082993507385253906 +10 5 6.12764 1.1276397705078125 1.271571452030912 +11 5 5.514374 0.514373779296875 0.26458038482815027 +18 6 5.71618652 0.2838134765625 0.080550089478492737 +20 8 5.861252 2.1387481689453125 4.574243730166927 +21 7 5.85632324 1.1436767578125 1.3079965263605118 +25 6 6.04560852 0.0456085205078125 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a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt new file mode 100644 index 0000000000..7d2d822e9a --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.281869324119046, or 0.280252574746412 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.583635 +L2(avg): 0.563155 +RMS(avg): 0.750436 +Loss-fn(avg): 0.563155 +R Squared: 0.281869 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.583635 (0.0000) +L2(avg): 0.563155 (0.0000) +RMS(avg): 0.750436 (0.0000) +Loss-fn(avg): 0.563155 (0.0000) +R Squared: 0.281869 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..705c51f87f --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.583635 0.563155 0.750436 0.563155 0.281869 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLS-TrainTest-wine.txt new file mode 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+4891 6 6.025711 0.0257110595703125 0.000661058584228158 +4892 5 5.619385 0.619384765625 0.38363748788833618 +4893 6 6.172577 0.172576904296875 0.029782787896692753 +4894 5 5.633911 0.6339111328125 0.40184332430362701 +4895 6 5.342102 0.65789794921875 0.43282971158623695 +4896 7 6.571274 0.4287261962890625 0.18380615138448775 +4897 6 6.34725952 0.347259521484375 0.1205891752615571 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-out.txt new file mode 100644 index 0000000000..46f60adaa5 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-out.txt @@ -0,0 +1,33 @@ +maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} +Not adding a normalizer. +Trainer solving for 12 parameters across 2409 examples +Coefficient of determination R2 = 0.291172889085396, or 0.287920032089126 (adjusted) +Not training a calibrator because it is not needed. +Not adding a normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.280282876617768, or 0.277086716602748 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.586799 +L2(avg): 0.573023 +RMS(avg): 0.756983 +Loss-fn(avg): 0.573023 +R Squared: 0.263873 +L1(avg): 0.588002 +L2(avg): 0.571858 +RMS(avg): 0.756213 +Loss-fn(avg): 0.571858 +R Squared: 0.276073 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.587400 (0.0006) +L2(avg): 0.572440 (0.0006) +RMS(avg): 0.756598 (0.0004) +Loss-fn(avg): 0.572440 (0.0006) +R Squared: 0.269973 (0.0061) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt new file mode 100644 index 0000000000..c6ae0bb553 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5874 0.57244 0.756598 0.57244 0.269973 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt new file mode 100644 index 0000000000..0ebf68c03b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.725769 0.27423095703125 0.075202617794275284 +6 6 5.47061157 0.529388427734375 0.28025210741907358 +8 6 5.16453552 0.8354644775390625 0.69800089322961867 +9 6 5.71276855 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0.30679861083626747 +4888 5 5.29177856 0.291778564453125 0.08513473067432642 +4889 6 5.71070862 0.2892913818359375 0.083689503604546189 +4894 5 5.566559 0.566558837890625 0.3209889167919755 +4896 7 6.59217834 0.4078216552734375 0.16631850250996649 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-out.txt new file mode 100644 index 0000000000..24c9756c70 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} +Not adding a normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.281870677180194, or 0.280253930853747 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.583635 +L2(avg): 0.563154 +RMS(avg): 0.750436 +Loss-fn(avg): 0.563154 +R Squared: 0.281871 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.583635 (0.0000) +L2(avg): 0.563154 (0.0000) +RMS(avg): 0.750436 (0.0000) +Loss-fn(avg): 0.563154 (0.0000) +R Squared: 0.281871 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..fe37f2c5f9 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSNorm-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.583635 0.563154 0.750436 0.563154 0.281871 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% 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0.18379306793212891 +4897 6 6.34736633 0.3473663330078125 0.12066336930729449 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-out.txt new file mode 100644 index 0000000000..095b8c8e84 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-out.txt @@ -0,0 +1,33 @@ +maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 2409 examples +Coefficient of determination R2 = 0.269438870320579, or 0.266086274397978 (adjusted) +Not training a calibrator because it is not needed. +Not adding a normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.255663034368455, or 0.252357541182364 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.596885 +L2(avg): 0.589177 +RMS(avg): 0.767579 +Loss-fn(avg): 0.589177 +R Squared: 0.243122 +L1(avg): 0.599038 +L2(avg): 0.587517 +RMS(avg): 0.766497 +Loss-fn(avg): 0.587517 +R Squared: 0.256250 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.597962 (0.0011) +L2(avg): 0.588347 (0.0008) +RMS(avg): 0.767038 (0.0005) +Loss-fn(avg): 0.588347 (0.0008) +R Squared: 0.249686 (0.0066) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt new file mode 100644 index 0000000000..f063507bbb --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSReg-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.597962 0.588347 0.767038 0.588347 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+4889 6 5.69565964 0.30434036254882813 0.092623056276352145 +4894 5 5.579279 0.57927894592285156 0.33556409718948998 +4896 7 6.57025433 0.42974567413330078 0.18468134443628514 diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt new file mode 100644 index 0000000000..6edb443e88 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.259289504308921, or 0.257621920303067 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.595209 +L2(avg): 0.580862 +RMS(avg): 0.762143 +Loss-fn(avg): 0.580862 +R Squared: 0.259290 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.595209 (0.0000) +L2(avg): 0.580862 (0.0000) +RMS(avg): 0.762143 (0.0000) +Loss-fn(avg): 0.580862 (0.0000) +R Squared: 0.259290 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..1707c73c50 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.595209 0.580862 0.762143 0.580862 0.25929 0.1 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /l2:0.1 + diff --git a/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt new file mode 100644 index 0000000000..b7b381bd97 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/OLS/OLSReg-TrainTest-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +0 6 5.58121967 0.41878032684326172 0.17537696215094911 +1 6 5.309566 0.69043397903442383 0.4766990794053072 +2 6 5.716072 0.28392791748046875 0.080615062324795872 +3 6 5.715474 0.28452587127685547 0.080954971425853728 +4 6 5.715474 0.28452587127685547 0.080954971425853728 +5 6 5.716072 0.28392791748046875 0.080615062324795872 +6 6 5.51660156 0.4833984375 0.23367404937744141 +7 6 5.58121967 0.41878032684326172 0.17537696215094911 +8 6 5.309566 0.69043397903442383 0.4766990794053072 +9 6 5.899788 0.10021209716796875 0.01004246441880241 +10 5 6.189271 1.1892709732055664 1.414365447709315 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a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..e8f77de3b1 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 132 | 2 | 0.9851 + negative || 8 | 212 | 0.9636 + ||====================== +Precision || 0.9429 | 0.9907 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.991045 +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 96 | 9 | 0.9143 + negative || 11 | 213 | 0.9509 + ||====================== +Precision || 0.8972 | 0.9595 | +OVERALL 0/1 ACCURACY: 0.939210 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.963435 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.977240 (0.0138) +Accuracy: 0.955481 (0.0163) +Positive precision: 0.920027 (0.0228) +Positive recall: 0.949680 (0.0354) +Negative precision: 0.975057 (0.0156) +Negative recall: 0.957265 (0.0064) +Log-loss: Infinity (NaN) +Log-loss reduction: -Infinity (NaN) +F1 Score: 0.934582 (0.0289) +AUPRC: 0.964431 (0.0168) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Preprocessing' started. +[1] 'Preprocessing' finished in %Time%. +[2] 'Training' started. +[2] 'Training' finished in %Time%. +[3] 'Preprocessing #2' started. +[3] 'Preprocessing #2' finished in %Time%. +[4] 'Training #2' started. +[4] 'Training #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..cbe85dbf29 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +SymSGD +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.97724 0.955481 0.920027 0.94968 0.975057 0.957265 Infinity -Infinity 0.934582 0.964431 1 SymSGD %Data% %Output% 99 0 0 maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 /nt:1 + diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt new file mode 100644 index 0000000000..d0e7499c6d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 1021.923 1 0 1 +6 0 101.91272 1 Infinity 1 +8 0 -334.817749 0 0 0 +9 0 -235.957581 0 0 0 +10 0 -272.79837 0 0 0 +11 0 -320.737427 0 0 0 +18 1 647.4429 1 0 1 +20 1 217.233337 1 0 1 +21 1 479.27478 1 0 1 +25 1 140.3844 1 0 1 +28 0 -320.737427 0 0 0 +31 0 -296.864 0 0 0 +32 1 454.251282 1 0 1 +35 0 -320.737427 0 0 0 +37 0 -78.9787 5.011722E-35 0 0 +40 0 +41 1 199.0091 1 0 1 +44 1 656.8247 1 0 1 +45 0 -322.804565 0 0 0 +46 1 682.001953 1 0 1 +48 0 -308.837372 0 0 0 +50 1 269.319824 1 0 1 +51 1 55.29657 1 0 1 +52 1 287.521057 1 0 1 +54 1 316.589355 1 0 1 +56 1 913.6865 1 0 1 +60 1 119.077576 1 0 1 +63 1 4.26116943 0.9860904 0.020208151201720485 1 +64 0 -325.252838 0 0 0 +66 0 -287.8332 0 0 0 +68 1 560.21106 1 0 1 +69 0 -288.1764 0 0 0 +70 0 -262.6084 0 0 0 +71 1 377.826416 1 0 1 +72 0 -7.10409546 0.00082105794 0.0011850227709561453 0 +73 1 361.900269 1 0 1 +74 1 308.8277 1 0 1 +76 0 -258.3786 0 0 0 +77 0 -166.195282 0 0 0 +79 0 -349.318481 0 0 0 +82 0 -207.568787 0 0 0 +88 0 -287.8332 0 0 0 +90 0 -301.379425 0 0 0 +91 0 -350.5975 0 0 0 +92 0 -287.8332 0 0 0 +93 0 -325.252838 0 0 0 +95 0 -301.379425 0 0 0 +96 0 -355.1129 0 0 0 +97 0 -283.3178 0 0 0 +98 1 380.50592 1 0 1 +99 1 571.659058 1 0 1 +100 1 51.7908325 1 0 1 +102 0 -265.5418 0 0 0 +104 1 527.381836 1 0 1 +105 1 1.51104736 0.8192164 0.28768354956614323 1 +106 1 975.2739 1 0 1 +108 0 -298.846741 0 0 0 +109 1 582.2739 1 0 1 +111 1 427.5822 1 0 1 +112 1 370.9096 1 0 1 +113 1 805.0089 1 0 1 +115 0 -169.885254 0 0 0 +117 1 521.3834 1 0 1 +120 0 -289.590118 0 0 0 +121 0 -167.273956 0 0 0 +122 1 864.813965 1 0 1 +123 1 263.304382 1 0 1 +125 0 -325.252838 0 0 0 +128 1 277.651428 1 0 1 +129 0 -577.7748 0 0 0 +131 0 -296.864 0 0 0 +132 1 762.661865 1 0 1 +133 0 -303.018982 0 0 0 +137 0 -340.095428 0 0 0 +138 0 -289.415222 0 0 0 +141 0 -344.61084 0 0 0 +144 0 -320.737427 0 0 0 +145 0 +147 0 -309.023651 0 0 0 +150 0 -272.79837 0 0 0 +151 1 249.55658 1 0 1 +152 1 884.802856 1 0 1 +154 0 -349.126221 0 0 0 +156 0 -227.212433 0 0 0 +161 0 -274.6302 0 0 0 +164 0 +167 1 283.316284 1 0 1 +169 0 -331.047241 0 0 0 +171 0 -301.379425 0 0 0 +173 1 1041.47913 1 0 1 +174 1 612.9641 1 0 1 +176 0 -296.864 0 0 0 +177 1 578.026 1 0 1 +179 1 180.726685 1 0 1 +180 0 -272.79837 0 0 0 +181 0 -349.126221 0 0 0 +183 1 834.9143 1 0 1 +187 1 544.907654 1 0 1 +188 1 709.276855 1 0 1 +189 0 -181.0476 0 0 0 +191 1 457.053833 1 0 1 +192 0 -307.1912 0 0 0 +196 0 384.403748 1 Infinity 1 +198 0 -349.126221 0 0 0 +199 0 -316.222015 0 0 0 +201 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0 0 +663 0 -271.4496 0 0 0 +664 0 -465.845337 0 0 0 +666 0 -209.536652 0 0 0 +667 0 -630.2707 0 0 0 +669 1 2239.64185 1 0 1 +671 0 -452.314178 0 0 0 +672 0 -617.483643 0 0 0 +673 0 -204.121124 0 0 0 +674 0 -515.32605 0 0 0 +675 0 -98.50183 1.667545E-43 0 0 +676 0 -393.387817 0 0 0 +677 0 -477.103638 0 0 0 +684 0 -425.9555 0 0 0 +686 0 -425.9555 0 0 0 +687 0 -377.613831 0 0 0 +690 0 -350.9007 0 0 0 +695 0 -438.7425 0 0 0 diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..d27eaf83bd --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 152 | 87 | 0.6360 + negative || 2 | 442 | 0.9955 + ||====================== +Precision || 0.9870 | 0.8355 | +OVERALL 0/1 ACCURACY: 0.869693 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.984941 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.984941 (0.0000) +Accuracy: 0.869693 (0.0000) +Positive precision: 0.987013 (0.0000) +Positive recall: 0.635983 (0.0000) +Negative precision: 0.835539 (0.0000) +Negative recall: 0.995495 (0.0000) +Log-loss: Infinity (0.0000) +Log-loss reduction: -Infinity (0.0000) +F1 Score: 0.773537 (0.0000) +AUPRC: 0.977633 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Preprocessing' started. +[1] 'Preprocessing' finished in %Time%. +[2] 'Training' started. +[2] 'Training' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..c056310ab0 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +SymSGD +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.984941 0.869693 0.987013 0.635983 0.835539 0.995495 Infinity -Infinity 0.773537 0.977633 1 SymSGD %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /nt:1 + diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt new file mode 100644 index 0000000000..8fb0a3fe22 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt @@ -0,0 +1,12 @@ +Linear Binary Classification Predictor non-zero weights + +(Bias) -448.1 +f1 49.29393 +f4 -25.15009 +f5 23.68305 +f3 16.76877 +f7 13.76585 +f6 -6.658058 +f8 4.843107 +f2 -3.424153 +f0 -0.3852913 diff --git a/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..f744bc3525 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SymSGD/SymSGD-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ 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normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.280280855195625, or 0.277084686203761 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.586798 +L2(avg): 0.573048 +RMS(avg): 0.756999 +Loss-fn(avg): 0.573048 +R Squared: 0.263841 +L1(avg): 0.587999 +L2(avg): 0.571859 +RMS(avg): 0.756214 +Loss-fn(avg): 0.571859 +R Squared: 0.276072 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.587398 (0.0006) +L2(avg): 0.572454 (0.0006) +RMS(avg): 0.756606 (0.0004) +Loss-fn(avg): 0.572454 (0.0006) +R Squared: 0.269956 (0.0061) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt new file mode 100644 index 0000000000..e3768ce894 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.587398 0.572454 0.756606 0.572454 0.269956 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt new file mode 100644 index 0000000000..c49fe65f22 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.72627258 0.2737274169921875 0.074926698813214898 +6 6 5.47047424 0.5295257568359375 0.28039752715267241 +8 6 5.164154 0.835845947265625 0.69863844756036997 +9 6 5.711914 0.2880859375 0.082993507385253906 +10 5 6.12764 1.1276397705078125 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0.5535888671875 0.30646063387393951 +4888 5 5.29202271 0.292022705078125 0.085277260281145573 +4889 6 5.710861 0.2891387939453125 0.08360124216414988 +4894 5 5.566284 0.5662841796875 0.32067777216434479 +4896 7 6.59217834 0.4078216552734375 0.16631850250996649 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt new file mode 100644 index 0000000000..7d2d822e9a --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.281869324119046, or 0.280252574746412 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.583635 +L2(avg): 0.563155 +RMS(avg): 0.750436 +Loss-fn(avg): 0.563155 +R Squared: 0.281869 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.583635 (0.0000) +L2(avg): 0.563155 (0.0000) +RMS(avg): 0.750436 (0.0000) +Loss-fn(avg): 0.563155 (0.0000) +R Squared: 0.281869 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..705c51f87f --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLS-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.583635 0.563155 0.750436 0.563155 0.281869 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS norm=No 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normalizer. +Trainer solving for 12 parameters across 2409 examples +Coefficient of determination R2 = 0.291172889085396, or 0.287920032089126 (adjusted) +Not training a calibrator because it is not needed. +Not adding a normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.280282876617768, or 0.277086716602748 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.586799 +L2(avg): 0.573023 +RMS(avg): 0.756983 +Loss-fn(avg): 0.573023 +R Squared: 0.263873 +L1(avg): 0.588002 +L2(avg): 0.571858 +RMS(avg): 0.756213 +Loss-fn(avg): 0.571858 +R Squared: 0.276073 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.587400 (0.0006) +L2(avg): 0.572440 (0.0006) +RMS(avg): 0.756598 (0.0004) +Loss-fn(avg): 0.572440 (0.0006) +R Squared: 0.269973 (0.0061) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt new file mode 100644 index 0000000000..c6ae0bb553 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.5874 0.57244 0.756598 0.57244 0.269973 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 xf=MinMax{col=Features} + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt new file mode 100644 index 0000000000..0ebf68c03b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 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normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.281870677180194, or 0.280253930853747 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.583635 +L2(avg): 0.563154 +RMS(avg): 0.750436 +Loss-fn(avg): 0.563154 +R Squared: 0.281871 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.583635 (0.0000) +L2(avg): 0.563154 (0.0000) +RMS(avg): 0.750436 (0.0000) +Loss-fn(avg): 0.563154 (0.0000) +R Squared: 0.281871 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..fe37f2c5f9 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.583635 0.563154 0.750436 0.563154 0.281871 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 xf=MinMax{col=Features} + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt new file mode 100644 index 0000000000..e296b7b161 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSNorm-TrainTest-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +0 6 5.562607 0.4373931884765625 0.19131280132569373 +1 6 5.216812 0.7831878662109375 0.61338323378004134 +2 6 5.766556 0.2334442138671875 0.054496200988069177 +3 6 5.7782135 0.2217864990234375 0.049189251149073243 +4 6 5.7782135 0.2217864990234375 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+4893 6 6.172577 0.172576904296875 0.029782787896692753 +4894 5 5.63417053 0.6341705322265625 0.40217226394452155 +4895 6 5.341858 0.65814208984375 0.4331510104238987 +4896 7 6.571289 0.4287109375 0.18379306793212891 +4897 6 6.34736633 0.3473663330078125 0.12066336930729449 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-out.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-out.txt new file mode 100644 index 0000000000..095b8c8e84 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-out.txt @@ -0,0 +1,33 @@ +maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 2409 examples +Coefficient of determination R2 = 0.269438870320579, or 0.266086274397978 (adjusted) +Not training a calibrator because it is not needed. +Not adding a normalizer. +Trainer solving for 12 parameters across 2489 examples +Coefficient of determination R2 = 0.255663034368455, or 0.252357541182364 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.596885 +L2(avg): 0.589177 +RMS(avg): 0.767579 +Loss-fn(avg): 0.589177 +R Squared: 0.243122 +L1(avg): 0.599038 +L2(avg): 0.587517 +RMS(avg): 0.766497 +Loss-fn(avg): 0.587517 +R Squared: 0.256250 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.597962 (0.0011) +L2(avg): 0.588347 (0.0008) +RMS(avg): 0.767038 (0.0005) +Loss-fn(avg): 0.588347 (0.0008) +R Squared: 0.249686 (0.0066) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt new file mode 100644 index 0000000000..f063507bbb --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.597962 0.588347 0.767038 0.588347 0.249686 0.1 OLS %Data% %Output% 99 0 0 maml.exe CV tr=OLS{l2=0.1} threads=- norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /l2:0.1 + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine.txt new file mode 100644 index 0000000000..9373538bea --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.68127632 0.31872367858886719 0.10158478329321952 +6 6 5.52236938 0.477630615234375 0.22813100460916758 +8 6 5.26242733 0.73757266998291016 0.5440134435057189 +9 6 5.86241531 0.13758468627929688 0.018929545898572542 +10 5 6.125346 1.1253461837768555 1.2664040333411322 +11 5 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+4883 6 6.033263 0.033263206481933594 0.001106440905459749 +4886 7 6.720747 0.27925300598144531 0.077982241349673131 +4887 7 6.57487 0.42512989044189453 0.18073542374713725 +4888 5 5.336135 0.33613491058349609 0.11298667811297491 +4889 6 5.69565964 0.30434036254882813 0.092623056276352145 +4894 5 5.579279 0.57927894592285156 0.33556409718948998 +4896 7 6.57025433 0.42974567413330078 0.18468134443628514 diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-out.txt new file mode 100644 index 0000000000..6edb443e88 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Trainer solving for 12 parameters across 4898 examples +Coefficient of determination R2 = 0.259289504308921, or 0.257621920303067 (adjusted) +Not training a calibrator because it is not needed. +L1(avg): 0.595209 +L2(avg): 0.580862 +RMS(avg): 0.762143 +Loss-fn(avg): 0.580862 +R Squared: 0.259290 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.595209 (0.0000) +L2(avg): 0.580862 (0.0000) +RMS(avg): 0.762143 (0.0000) +Loss-fn(avg): 0.580862 (0.0000) +R Squared: 0.259290 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..1707c73c50 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +OLS +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /l2 Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.595209 0.580862 0.762143 0.580862 0.25929 0.1 OLS %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=OLS{l2=0.1} norm=No dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /l2:0.1 + diff --git a/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt new file mode 100644 index 0000000000..b7b381bd97 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/OLS/OLSReg-TrainTest-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +0 6 5.58121967 0.41878032684326172 0.17537696215094911 +1 6 5.309566 0.69043397903442383 0.4766990794053072 +2 6 5.716072 0.28392791748046875 0.080615062324795872 +3 6 5.715474 0.28452587127685547 0.080954971425853728 +4 6 5.715474 0.28452587127685547 0.080954971425853728 +5 6 5.716072 0.28392791748046875 0.080615062324795872 +6 6 5.51660156 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0.38198769241353148 +4896 7 6.55421925 0.44578075408935547 0.19872048071647441 +4897 6 6.25601768 0.25601768493652344 0.06554505500025698 diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..e8f77de3b1 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 132 | 2 | 0.9851 + negative || 8 | 212 | 0.9636 + ||====================== +Precision || 0.9429 | 0.9907 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.991045 +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 96 | 9 | 0.9143 + negative || 11 | 213 | 0.9509 + ||====================== +Precision || 0.8972 | 0.9595 | +OVERALL 0/1 ACCURACY: 0.939210 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.963435 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.977240 (0.0138) +Accuracy: 0.955481 (0.0163) +Positive precision: 0.920027 (0.0228) +Positive recall: 0.949680 (0.0354) +Negative precision: 0.975057 (0.0156) +Negative recall: 0.957265 (0.0064) +Log-loss: Infinity (NaN) +Log-loss reduction: -Infinity (NaN) +F1 Score: 0.934582 (0.0289) +AUPRC: 0.964431 (0.0168) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Preprocessing' started. +[1] 'Preprocessing' finished in %Time%. +[2] 'Training' started. +[2] 'Training' finished in %Time%. +[3] 'Preprocessing #2' started. +[3] 'Preprocessing #2' finished in %Time%. +[4] 'Training #2' started. +[4] 'Training #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..cbe85dbf29 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +SymSGD +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.97724 0.955481 0.920027 0.94968 0.975057 0.957265 Infinity -Infinity 0.934582 0.964431 1 SymSGD %Data% %Output% 99 0 0 maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 /nt:1 + diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt new file mode 100644 index 0000000000..d0e7499c6d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 1021.923 1 0 1 +6 0 101.91272 1 Infinity 1 +8 0 -334.817749 0 0 0 +9 0 -235.957581 0 0 0 +10 0 -272.79837 0 0 0 +11 0 -320.737427 0 0 0 +18 1 647.4429 1 0 1 +20 1 217.233337 1 0 1 +21 1 479.27478 1 0 1 +25 1 140.3844 1 0 1 +28 0 -320.737427 0 0 0 +31 0 -296.864 0 0 0 +32 1 454.251282 1 0 1 +35 0 -320.737427 0 0 0 +37 0 -78.9787 5.011722E-35 0 0 +40 0 +41 1 199.0091 1 0 1 +44 1 656.8247 1 0 1 +45 0 -322.804565 0 0 0 +46 1 682.001953 1 0 1 +48 0 -308.837372 0 0 0 +50 1 269.319824 1 0 1 +51 1 55.29657 1 0 1 +52 1 287.521057 1 0 1 +54 1 316.589355 1 0 1 +56 1 913.6865 1 0 1 +60 1 119.077576 1 0 1 +63 1 4.26116943 0.9860904 0.020208151201720485 1 +64 0 -325.252838 0 0 0 +66 0 -287.8332 0 0 0 +68 1 560.21106 1 0 1 +69 0 -288.1764 0 0 0 +70 0 -262.6084 0 0 0 +71 1 377.826416 1 0 1 +72 0 -7.10409546 0.00082105794 0.0011850227709561453 0 +73 1 361.900269 1 0 1 +74 1 308.8277 1 0 1 +76 0 -258.3786 0 0 0 +77 0 -166.195282 0 0 0 +79 0 -349.318481 0 0 0 +82 0 -207.568787 0 0 0 +88 0 -287.8332 0 0 0 +90 0 -301.379425 0 0 0 +91 0 -350.5975 0 0 0 +92 0 -287.8332 0 0 0 +93 0 -325.252838 0 0 0 +95 0 -301.379425 0 0 0 +96 0 -355.1129 0 0 0 +97 0 -283.3178 0 0 0 +98 1 380.50592 1 0 1 +99 1 571.659058 1 0 1 +100 1 51.7908325 1 0 1 +102 0 -265.5418 0 0 0 +104 1 527.381836 1 0 1 +105 1 1.51104736 0.8192164 0.28768354956614323 1 +106 1 975.2739 1 0 1 +108 0 -298.846741 0 0 0 +109 1 582.2739 1 0 1 +111 1 427.5822 1 0 1 +112 1 370.9096 1 0 1 +113 1 805.0089 1 0 1 +115 0 -169.885254 0 0 0 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0 0 0 +278 0 -301.379425 0 0 0 +279 1 438.823853 1 0 1 +280 0 -292.348633 0 0 0 +283 1 555.4083 1 0 1 +284 1 445.768616 1 0 1 +285 1 1001.10779 1 0 1 +288 1 54.43335 1 0 1 +290 0 -349.126221 0 0 0 +291 0 -301.379425 0 0 0 +293 1 386.6949 1 0 1 +296 0 139.220642 1 Infinity 1 +297 0 +299 1 227.814941 1 0 1 +300 1 407.6792 1 0 1 +301 0 -301.379425 0 0 0 +303 0 -301.379425 0 0 0 +304 1 265.7011 1 0 1 +308 1 588.7855 1 0 1 +309 0 -65.36084 4.11289773E-29 0 0 +311 0 -349.126221 0 0 0 +312 1 -94.50748 9.035572E-42 136.34536397147204 0 +314 0 -296.671753 0 0 0 +316 1 466.170166 1 0 1 +317 1 736.0132 1 0 1 +319 0 161.598083 1 Infinity 1 +321 0 +323 1 388.03302 1 0 1 +327 0 -325.252838 0 0 0 +328 1 584.984 1 0 1 +329 1 459.499573 1 0 1 +331 0 -280.869537 0 0 0 +332 0 -206.44986 0 0 0 +333 1 399.285278 1 0 1 +336 1 425.5838 1 0 1 +338 0 -296.671753 0 0 0 +343 0 -349.126221 0 0 0 +344 1 473.410339 1 0 1 +346 0 -245.701279 0 0 0 +347 0 -294.1391 0 0 0 +348 1 -152.083282 0 Infinity 0 +349 1 237.0896 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-271.4496 0 0 0 +663 0 -271.4496 0 0 0 +664 0 -465.845337 0 0 0 +666 0 -209.536652 0 0 0 +667 0 -630.2707 0 0 0 +669 1 2239.64185 1 0 1 +671 0 -452.314178 0 0 0 +672 0 -617.483643 0 0 0 +673 0 -204.121124 0 0 0 +674 0 -515.32605 0 0 0 +675 0 -98.50183 1.667545E-43 0 0 +676 0 -393.387817 0 0 0 +677 0 -477.103638 0 0 0 +684 0 -425.9555 0 0 0 +686 0 -425.9555 0 0 0 +687 0 -377.613831 0 0 0 +690 0 -350.9007 0 0 0 +695 0 -438.7425 0 0 0 diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..d27eaf83bd --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Data fully loaded into memory. +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 152 | 87 | 0.6360 + negative || 2 | 442 | 0.9955 + ||====================== +Precision || 0.9870 | 0.8355 | +OVERALL 0/1 ACCURACY: 0.869693 +LOG LOSS/instance: Infinity +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): -Infinity +AUC: 0.984941 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.984941 (0.0000) +Accuracy: 0.869693 (0.0000) +Positive precision: 0.987013 (0.0000) +Positive recall: 0.635983 (0.0000) +Negative precision: 0.835539 (0.0000) +Negative recall: 0.995495 (0.0000) +Log-loss: Infinity (0.0000) +Log-loss reduction: -Infinity (0.0000) +F1 Score: 0.773537 (0.0000) +AUPRC: 0.977633 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Preprocessing' started. +[1] 'Preprocessing' finished in %Time%. +[2] 'Training' started. +[2] 'Training' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..c056310ab0 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +SymSGD +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.984941 0.869693 0.987013 0.635983 0.835539 0.995495 Infinity -Infinity 0.773537 0.977633 1 SymSGD %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /nt:1 + diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt new file mode 100644 index 0000000000..8fb0a3fe22 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt @@ -0,0 +1,12 @@ +Linear Binary Classification Predictor non-zero weights + +(Bias) -448.1 +f1 49.29393 +f4 -25.15009 +f5 23.68305 +f3 16.76877 +f7 13.76585 +f6 -6.658058 +f8 4.843107 +f2 -3.424153 +f0 -0.3852913 diff --git a/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..f744bc3525 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SymSGD/SymSGD-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -415.3703 0 0 0 +1 0 -109.523041 0 0 0 +2 0 -390.916656 0 0 0 +3 0 33.8270569 1 Infinity 1 +4 0 -381.447449 0 0 0 +5 1 243.726929 1 0 1 +6 0 -200.681656 0 0 0 +7 0 -417.63858 0 0 0 +8 0 -381.525879 0 0 0 +9 0 -359.03302 0 0 0 +10 0 -388.679047 0 0 0 +11 0 -407.556366 0 0 0 +12 1 -208.0876 0 Infinity 0 +13 0 -366.463 0 0 0 +14 1 136.450256 1 0 1 +15 1 -314.800262 0 Infinity 0 +16 0 -408.326935 0 0 0 +17 0 -414.985 0 0 0 +18 1 102.798035 1 0 1 +19 0 -415.755585 0 0 0 +20 1 10.1497192 0.9999609 5.6411412351548271E-05 1 +21 1 -61.521698 1.91190378E-27 88.757048641689195 0 +22 0 -407.94165 0 0 0 +23 1 +24 0 -413.829132 0 0 0 +25 1 -111.690765 0 Infinity 0 +26 0 -333.49762 0 0 0 +27 0 -408.712219 0 0 0 +28 0 -407.556366 0 0 0 +29 0 -407.361328 0 0 0 +30 0 -382.791565 0 0 0 +31 0 -414.214417 0 0 0 +32 1 -140.35733 0 Infinity 0 +33 0 -397.445648 0 0 0 +34 0 -411.365784 0 0 0 +35 0 -407.556366 0 0 0 +36 1 89.1488 1 0 1 +37 0 -367.9438 0 0 0 +38 1 125.049805 1 0 1 +39 1 -120.420319 0 Infinity 0 +40 0 +41 1 -214.114883 0 Infinity 0 +42 1 86.67383 1 0 1 +43 1 -299.954132 0 Infinity 0 +44 1 -14.4605713 5.246304E-07 20.86219519410675 0 +45 0 -402.327942 0 0 0 +46 1 139.792358 1 0 1 +47 0 -407.171082 0 0 0 +48 0 -381.447449 0 0 0 +49 1 141.825745 1 0 1 +50 1 -170.3082 0 Infinity 0 +51 1 -160.977692 0 Infinity 0 +52 1 -127.401 0 Infinity 0 +53 1 -119.971008 0 Infinity 0 +54 1 -161.725189 0 Infinity 0 +55 1 -41.1490173 1.34650766E-18 59.365483272233966 0 +56 1 199.693787 1 0 1 +57 1 -410.444183 0 Infinity 0 +58 1 -273.693665 0 Infinity 0 +59 1 -206.296631 0 Infinity 0 +60 1 -123.953339 0 Infinity 0 +61 0 -383.488 0 0 0 +62 1 -11.9711 6.324332E-06 17.270655479328113 0 +63 1 -269.080566 0 Infinity 0 +64 0 -407.171082 0 0 0 +65 1 -209.844543 0 Infinity 0 +66 0 -414.985 0 0 0 +67 1 -161.339172 0 Infinity 0 +68 1 -58.4673157 4.05478141E-26 84.350506323497086 0 +69 0 -400.063263 0 0 0 +70 0 -415.560547 0 0 0 +71 1 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0 -158.312744 0 0 0 +416 1 -78.57623 7.495069E-35 113.36154154761103 0 +417 0 -407.171082 0 0 0 +418 0 -310.439728 0 0 0 +419 0 -377.760681 0 0 0 +420 0 -287.8263 0 0 0 +421 1 88.56616 1 0 1 +422 0 -295.7137 0 0 0 +423 0 -415.560547 0 0 0 +424 0 -401.2836 0 0 0 +425 1 236.068481 1 0 1 +426 0 -320.587067 0 0 0 +427 1 -44.00345 7.754345E-20 63.483556937036411 0 +428 0 -407.171082 0 0 0 +429 0 -407.556366 0 0 0 +430 0 -294.817322 0 0 0 +431 0 -418.3671 0 0 0 +432 0 -394.946381 0 0 0 +433 0 -321.876282 0 0 0 +434 0 122.56488 1 Infinity 1 +435 1 71.62463 1 0 1 +436 1 -2.05007935 0.114044361 3.1323329838202993 0 +437 0 -401.668884 0 0 0 +438 0 -374.979675 0 0 0 +439 0 -402.054169 0 0 0 +440 1 -9.103485 0.000111264941 13.133713305553275 0 +441 0 -234.828949 0 0 0 +442 0 -319.609375 0 0 0 +443 0 -376.829956 0 0 0 +444 0 -350.0185 0 0 0 +445 0 -400.898315 0 0 0 +446 0 -400.513 0 0 0 +447 0 -402.054169 0 0 0 +448 0 -375.3629 0 0 0 +449 1 173.530884 1 0 1 +450 0 -349.6393 0 0 0 +451 0 -402.054169 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0.00048102966772982418 1 +531 0 -362.457153 0 0 0 +532 0 -388.679047 0 0 0 +533 0 -407.94165 0 0 0 +534 0 -407.556366 0 0 0 +535 0 -403.908661 0 0 0 +536 0 -415.3703 0 0 0 +537 0 -418.794434 0 0 0 +538 0 -408.326935 0 0 0 +539 0 -409.097534 0 0 0 +540 0 -385.029175 0 0 0 +541 0 -401.2836 0 0 0 +542 0 -303.4663 0 0 0 +543 0 -408.326935 0 0 0 +544 0 -397.6359 0 0 0 +545 0 -408.712219 0 0 0 +546 1 408.09906 1 0 1 +547 0 -375.7482 0 0 0 +548 0 -376.133484 0 0 0 +549 1 141.684875 1 0 1 +550 0 -407.94165 0 0 0 +551 0 -413.829132 0 0 0 +552 0 -344.853363 0 0 0 +553 0 -164.293976 0 0 0 +554 0 -401.2836 0 0 0 +555 0 -226.694183 0 0 0 +556 0 -320.923584 0 0 0 +557 0 -368.516632 0 0 0 +558 0 -407.556366 0 0 0 +559 0 -408.712219 0 0 0 +560 0 -415.3703 0 0 0 +561 0 -415.3703 0 0 0 +562 0 -413.829132 0 0 0 +563 0 -407.94165 0 0 0 +564 0 -401.219147 0 0 0 +565 1 215.478271 1 0 1 +566 0 -418.023865 0 0 0 +567 0 -360.960815 0 0 0 +568 1 -99.8051453 4.484155E-44 144 0 +569 1 128.35553 1 0 1 +570 1 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2.01274975E-21 68.751322185612878 0 +606 0 -384.900116 0 0 0 +607 0 -400.513 0 0 0 +608 1 113.71698 1 0 1 +609 0 -402.054169 0 0 0 +610 1 40.86389 1 0 1 +611 1 118.382507 1 0 1 +612 1 380.642151 1 0 1 +613 0 -308.9685 0 0 0 +614 0 -382.40625 0 0 0 +615 0 -415.175232 0 0 0 +616 0 -407.94165 0 0 0 +617 0 +618 0 -408.326935 0 0 0 +619 0 -408.712219 0 0 0 +620 0 -407.94165 0 0 0 +621 0 -311.9197 0 0 0 +622 0 -379.369446 0 0 0 +623 0 -400.513 0 0 0 +624 0 -366.793365 0 0 0 +625 0 -362.232849 0 0 0 +626 1 -60.24713 6.839168E-27 86.918237673162878 0 +627 0 -306.166077 0 0 0 +628 0 -400.898315 0 0 0 +629 0 -401.668884 0 0 0 +630 0 -359.993835 0 0 0 +631 0 -408.712219 0 0 0 +632 0 -400.513 0 0 0 +633 1 1.76364136 0.8536651 0.22825787272289805 1 +634 0 -401.2836 0 0 0 +635 0 -411.55603 0 0 0 +636 1 84.4071045 1 0 1 +637 0 -312.084869 0 0 0 +638 0 -401.668884 0 0 0 +639 0 -368.516632 0 0 0 +640 0 -368.131348 0 0 0 +641 0 -407.94165 0 0 0 +642 0 -407.94165 0 0 0 +643 0 -400.513 0 0 0 +644 0 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+686 0 -400.513 0 0 0 +687 0 -380.409943 0 0 0 +688 0 -401.668884 0 0 0 +689 0 -366.611267 0 0 0 +690 0 -366.975464 0 0 0 +691 1 143.604248 1 0 1 +692 0 -401.2836 0 0 0 +693 0 -403.098541 0 0 0 +694 0 -402.750641 0 0 0 +695 0 -400.898315 0 0 0 +696 1 48.056427 1 0 1 +697 1 31.3800049 1 0 1 +698 1 12.3017273 0.99999547 6.5353555352246199E-06 1 diff --git a/test/Directory.Build.props b/test/Directory.Build.props index ee5d507566..2e16be2f2b 100644 --- a/test/Directory.Build.props +++ b/test/Directory.Build.props @@ -20,7 +20,7 @@ $(ToolsDir)Test.snk - + diff --git a/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj b/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj index ea92975b35..6b99866749 100644 --- a/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj +++ b/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj @@ -5,10 +5,12 @@ + + diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/ColumnTypes.cs b/test/Microsoft.ML.Core.Tests/UnitTests/ColumnTypes.cs new file mode 100644 index 0000000000..f3cfa8c270 --- /dev/null +++ b/test/Microsoft.ML.Core.Tests/UnitTests/ColumnTypes.cs @@ -0,0 +1,101 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.ImageAnalytics; +using Xunit; +namespace Microsoft.ML.Runtime.RunTests +{ + public sealed class ColumnTypeTests + { + [Fact] + public void TestEqualAndGetHashCode() + { + var dict = new Dictionary(); + // add PrimitiveTypes, KeyType & corresponding VectorTypes + PrimitiveType tmp; + VectorType tmp1, tmp2; + foreach (var kind in (DataKind[])Enum.GetValues(typeof(DataKind))) + { + tmp = PrimitiveType.FromKind(kind); + if(dict.ContainsKey(tmp) && dict[tmp] != tmp.ToString()) + Assert.True(false, dict[tmp] + " and " + tmp.ToString() + " are duplicates."); + dict[tmp] = tmp.ToString(); + for (int size = 0; size < 5; size++) + { + tmp1 = new VectorType(tmp, size); + if (dict.ContainsKey(tmp1) && dict[tmp1] != tmp1.ToString()) + Assert.True(false, dict[tmp1] + " and " + tmp1.ToString() + " are duplicates."); + dict[tmp1] = tmp1.ToString(); + for (int size1 = 0; size1 < 5; size1++) + { + tmp2 = new VectorType(tmp, size, size1); + if (dict.ContainsKey(tmp2) && dict[tmp2] != tmp2.ToString()) + Assert.True(false, dict[tmp2] + " and " + tmp2.ToString() + " are duplicates."); + dict[tmp2] = tmp2.ToString(); + } + } + + // KeyType & Vector + if (!KeyType.IsValidDataKind(kind)) + continue; + for (ulong min = 0; min < 5; min++) + { + for (var count = 0; count < 5; count++) + { + tmp = new KeyType(kind, min, count); + if (dict.ContainsKey(tmp) && dict[tmp] != tmp.ToString()) + Assert.True(false, dict[tmp] + " and " + tmp.ToString() + " are duplicates."); + dict[tmp] = tmp.ToString(); + for (int size = 0; size < 5; size++) + { + tmp1 = new VectorType(tmp, size); + if (dict.ContainsKey(tmp1) && dict[tmp1] != tmp1.ToString()) + Assert.True(false, dict[tmp1] + " and " + tmp1.ToString() + " are duplicates."); + dict[tmp1] = tmp1.ToString(); + for (int size1 = 0; size1 < 5; size1++) + { + tmp2 = new VectorType(tmp, size, size1); + if (dict.ContainsKey(tmp2) && dict[tmp2] != tmp2.ToString()) + Assert.True(false, dict[tmp2] + " and " + tmp2.ToString() + " are duplicates."); + dict[tmp2] = tmp2.ToString(); + } + } + } + tmp = new KeyType(kind, min, 0, false); + if (dict.ContainsKey(tmp) && dict[tmp] != tmp.ToString()) + Assert.True(false, dict[tmp] + " and " + tmp.ToString() + " are duplicates."); + dict[tmp] = tmp.ToString(); + for (int size = 0; size < 5; size++) + { + tmp1 = new VectorType(tmp, size); + if (dict.ContainsKey(tmp1) && dict[tmp1] != tmp1.ToString()) + Assert.True(false, dict[tmp1] + " and " + tmp1.ToString() + " are duplicates."); + dict[tmp1] = tmp1.ToString(); + for (int size1 = 0; size1 < 5; size1++) + { + tmp2 = new VectorType(tmp, size, size1); + if (dict.ContainsKey(tmp2) && dict[tmp2] != tmp2.ToString()) + Assert.True(false, dict[tmp2] + " and " + tmp2.ToString() + " are duplicates."); + dict[tmp2] = tmp2.ToString(); + } + } + } + } + + // add ImageTypes + for (int height = 1; height < 5; height++) + for (int width = 1; width < 5; width++) + { + var tmp4 = new ImageType(height, width); + if (dict.ContainsKey(tmp4)) + Assert.True(false, dict[tmp4] + " and " + tmp4.ToString() + " are duplicates."); + dict[tmp4] = tmp4.ToString(); + } + } + } +} diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs index b0bc269164..c44678bf07 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs @@ -265,6 +265,53 @@ private string GetBuildPrefix() #endif } + [Fact(Skip = "Execute this test if you want to regenerate ep-list and _manifest.json")] + public void RegenerateEntryPointCatalog() + { + var buildPrefix = GetBuildPrefix(); + var epListFile = buildPrefix + "_ep-list.tsv"; + var manifestFile = buildPrefix + "_manifest.json"; + + var entryPointsSubDir = Path.Combine("..", "Common", "EntryPoints"); + var catalog = ModuleCatalog.CreateInstance(Env); + var epListPath = GetBaselinePath(entryPointsSubDir, epListFile); + DeleteOutputPath(epListPath); + + var regex = new Regex(@"\r\n?|\n", RegexOptions.Compiled); + File.WriteAllLines(epListPath, catalog.AllEntryPoints() + .Select(x => string.Join("\t", + x.Name, + regex.Replace(x.Description, ""), + x.Method.DeclaringType, + x.Method.Name, + x.InputType, + x.OutputType) + .Replace(Environment.NewLine, "")) + .OrderBy(x => x)); + + + var jObj = JsonManifestUtils.BuildAllManifests(Env, catalog); + + //clean up the description from the new line characters + if (jObj[FieldNames.TopEntryPoints] != null && jObj[FieldNames.TopEntryPoints] is JArray) + { + foreach (JToken entry in jObj[FieldNames.TopEntryPoints].Children()) + if (entry[FieldNames.Desc] != null) + entry[FieldNames.Desc] = regex.Replace(entry[FieldNames.Desc].ToString(), ""); + } + var manifestPath = GetBaselinePath(entryPointsSubDir, manifestFile); + DeleteOutputPath(manifestPath); + + using (var file = File.OpenWrite(manifestPath)) + using (var writer = new StreamWriter(file)) + using (var jw = new JsonTextWriter(writer)) + { + jw.Formatting = Formatting.Indented; + jObj.WriteTo(jw); + } + } + + [Fact] public void EntryPointCatalog() { @@ -3710,5 +3757,52 @@ public void EntryPointTreeLeafFeaturizer() } } } + + [Fact] + public void EntryPointWordEmbeddings() + { + string dataFile = DeleteOutputPath("SavePipe", "SavePipeTextWordEmbeddings-SampleText.txt"); + File.WriteAllLines(dataFile, new[] { + "The quick brown fox jumps over the lazy dog.", + "The five boxing wizards jump quickly." + }); + var inputFile = new SimpleFileHandle(Env, dataFile, false, false); + var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + SeparatorChars = new []{' '}, + Column = new[] + { + new TextLoader.Column() + { + Name = "Text", + Source = new [] { new TextLoader.Range() { Min = 0, VariableEnd=true, ForceVector=true} }, + Type = DataKind.Text + } + } + }, + InputFile = inputFile, + }).Data; + var embedding = Transforms.TextAnalytics.WordEmbeddings(Env, new WordEmbeddingsTransform.Arguments() + { + Data = dataView, + Column = new[] { new WordEmbeddingsTransform.Column { Name = "Features", Source = "Text" } }, + ModelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe + }); + var result = embedding.OutputData; + using (var cursor = result.GetRowCursor((x => true))) + { + Assert.True(result.Schema.TryGetColumnIndex("Features", out int featColumn)); + var featGetter = cursor.GetGetter>(featColumn); + VBuffer feat = default; + while (cursor.MoveNext()) + { + featGetter(ref feat); + Assert.True(feat.Count == 150); + Assert.True(feat.Values[0] != 0); + } + } + } } } \ No newline at end of file diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/CpuMathNativeUtils.cs b/test/Microsoft.ML.CpuMath.PerformanceTests/CpuMathNativeUtils.cs new file mode 100644 index 0000000000..8df3352556 --- /dev/null +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/CpuMathNativeUtils.cs @@ -0,0 +1,89 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +// The exported function names need to be unique (can't be disambiguated based on signature), hence +// we introduce suffix letters to indicate the general patterns used. +// * A suffix means aligned and padded for SSE operations. +// * U suffix means unaligned and unpadded. +// * S suffix means sparse (unaligned) vector. +// * P suffix means sparse (unaligned) partial vector - the vector is only part of a larger sparse vector. +// * R suffix means sparse matrix. +// * C suffix means convolution matrix. +// * D suffix means convolution matrix, with implicit source padding. +// * Tran means the matrix is transposed. + +using System.Runtime.InteropServices; +using System.Security; + +namespace Microsoft.ML.CpuMath.PerformanceTests +{ + internal static class CpuMathNativeUtils + { + [DllImport("CpuMathNative", EntryPoint = "AddScalarU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float AddScalarU(float a, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "ScaleU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void ScaleU(float a, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "ScaleSrcU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void ScaleSrcU(float a, /*_In_ const*/ float* ps, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "ScaleAddU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void ScaleAddU(float a, float b, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "AddScaleU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void AddScaleU(float a, /*_In_ const*/ float* ps, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "AddScaleSU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void AddScaleSU(float a, /*_In_ const*/ float* ps, /*_In_ const*/ int* pi, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "AddScaleCopyU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void AddScaleCopyU(float a, /*_In_ const*/ float* ps, /*_In_ const*/ float* pd, /*_Inout_*/ float* pr, int c); + + [DllImport("CpuMathNative", EntryPoint = "AddU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void AddU(/*_In_ const*/ float* ps, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "AddSU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void AddSU(/*_In_ const*/ float* ps, /*_In_ const*/ int* pi, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "MulElementWiseU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void MulElementWiseU(/*_In_ const*/ float* ps1, /*_In_ const*/ float* ps2, /*_Inout_*/ float* pd, int c); + + [DllImport("CpuMathNative", EntryPoint = "SumU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float SumU(/*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "SumSqU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float SumSqU(/*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "SumSqDiffU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float SumSqDiffU(float mean, /*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "SumAbsU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float SumAbsU(/*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "SumAbsDiffU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float SumAbsDiffU(float mean, /*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "MaxAbsU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float MaxAbsU(/*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "MaxAbsDiffU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float MaxAbsDiffU(float mean, /*const*/ float* ps, int c); + + [DllImport("CpuMathNative", EntryPoint = "DotU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float DotU(/*const*/ float* pa, /*const*/ float* pb, int c); + + [DllImport("CpuMathNative", EntryPoint = "DotSU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float DotSU(/*const*/ float* pa, /*const*/ float* pb, /*const*/ int* pi, int c); + + [DllImport("CpuMathNative", EntryPoint = "Dist2"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe float Dist2(/*const*/ float* px, /*const*/ float* py, int c); + + [DllImport("CpuMathNative", EntryPoint = "SdcaL1UpdateU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void SdcaL1UpdateU(float primalUpdate, /*_In_ const*/ float* ps, float threshold, /*_Inout_*/ float* pd1, /*_Inout_*/ float* pd2, int c); + + [DllImport("CpuMathNative", EntryPoint = "SdcaL1UpdateSU"), SuppressUnmanagedCodeSecurity] + internal static extern unsafe void SdcaL1UpdateSU(float primalUpdate, /*_In_ const*/ float* ps, /*_In_ const*/ int* pi, float threshold, /*_Inout_*/ float* pd1, /*_Inout_*/ float* pd2, int c); + } +} diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj b/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj new file mode 100644 index 0000000000..61d22bbfbb --- /dev/null +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/Microsoft.ML.CpuMath.PerformanceTests.csproj @@ -0,0 +1,27 @@ + + + Exe + 7.2 + Microsoft.ML.CpuMath.PerformanceTests.Program + netcoreapp3.0 + true + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/Program.cs b/test/Microsoft.ML.CpuMath.PerformanceTests/Program.cs new file mode 100644 index 0000000000..cd731e8cf3 --- /dev/null +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/Program.cs @@ -0,0 +1,29 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using BenchmarkDotNet.Configs; +using BenchmarkDotNet.Jobs; +using BenchmarkDotNet.Running; +using BenchmarkDotNet.Toolchains.InProcess; + +namespace Microsoft.ML.CpuMath.PerformanceTests +{ + class Program + { + public static void Main(string[] args) + { + BenchmarkSwitcher + .FromAssembly(typeof(Program).Assembly) + .Run(null, CreateClrVsCoreConfig()); + } + + private static IConfig CreateClrVsCoreConfig() + { + var config = DefaultConfig.Instance.With( + Job.ShortRun. + With(InProcessToolchain.Instance)); + return config; + } + } +} diff --git a/test/Microsoft.ML.CpuMath.PerformanceTests/SsePerformanceTests.cs b/test/Microsoft.ML.CpuMath.PerformanceTests/SsePerformanceTests.cs new file mode 100644 index 0000000000..ade2ea6a0e --- /dev/null +++ b/test/Microsoft.ML.CpuMath.PerformanceTests/SsePerformanceTests.cs @@ -0,0 +1,386 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using BenchmarkDotNet.Attributes; +using BenchmarkDotNet.Running; +using Microsoft.ML.Runtime.Internal.CpuMath; + +namespace Microsoft.ML.CpuMath.PerformanceTests +{ + public class SsePerformanceTests + { + private const int EXP_MAX = 127; + private const int EXP_MIN = 0; + + private const int IDXLEN = 1000003; + private const int LEN = 1000003; + private const int EXP_RANGE = EXP_MAX / 2; + private const int DEFAULT_SEED = 253421; + private const float DEFAULT_SCALE = 1.11f; + private const int DEFAULT_CROW = 500; + private const int DEFAULT_CCOL = 2000; + private const bool ADD = true; + + private float[] src, dst, original, src1, src2, result; + private int[] idx; + private int seed = DEFAULT_SEED; + + private static float NextFloat(Random rand, int expRange) + { + double mantissa = (rand.NextDouble() * 2.0) - 1.0; + double exponent = Math.Pow(2.0, rand.Next(-expRange + 1, expRange + 1)); + return (float)(mantissa * exponent); + } + + private static int GetSeed() + { + int seed = DEFAULT_SEED; + + if (Environment.GetEnvironmentVariable("CPUMATH_SEED") != null) + { + string CPUMATH_SEED = Environment.GetEnvironmentVariable("CPUMATH_SEED"); + + if (!int.TryParse(CPUMATH_SEED, out seed)) + { + if(string.Equals(CPUMATH_SEED, "random", StringComparison.OrdinalIgnoreCase)) + { + seed = new Random().Next(); + } + else + { + seed = DEFAULT_SEED; + } + } + } + + Console.WriteLine("Random seed: " + seed + "; set environment variable CPUMATH_SEED to this value to reproduce results"); + + return seed; + } + + [GlobalSetup] + public void Setup() + { + src = new float[LEN]; + dst = new float[LEN]; + src1 = new float[LEN]; + src2 = new float[LEN]; + original = new float[LEN]; + result = new float[LEN]; + idx = new int[IDXLEN]; + + seed = GetSeed(); + Random rand = new Random(seed); + + for (int i = 0; i < LEN; i++) + { + src[i] = NextFloat(rand, EXP_RANGE); + dst[i] = NextFloat(rand, EXP_RANGE); + original[i] = dst[i]; + result[i] = dst[i]; + src1[i] = NextFloat(rand, EXP_RANGE); + src2[i] = NextFloat(rand, EXP_RANGE); + } + + for (int i = 0; i < IDXLEN; i++) + { + idx[i] = rand.Next(0, LEN); + } + } + + [GlobalCleanup] + public void GlobalCleanup() + { + original.CopyTo(dst, 0); + original.CopyTo(result, 0); + } + + [Benchmark] + public unsafe float NativeAddScalarUPerf() + { + fixed (float* pdst = dst) + { + return CpuMathNativeUtils.AddScalarU(DEFAULT_SCALE, pdst, LEN); + } + } + + [Benchmark] + public void ManagedAddScalarUPerf() => CpuMathUtils.Add(DEFAULT_SCALE, dst, LEN); + + [Benchmark] + public unsafe void NativeScaleUPerf() + { + fixed (float* pdst = dst) + { + CpuMathNativeUtils.ScaleU(DEFAULT_SCALE, pdst, LEN); + } + } + + [Benchmark] + public void ManagedScaleUPerf() => CpuMathUtils.Scale(DEFAULT_SCALE, dst, LEN); + + [Benchmark] + public unsafe void NativeScaleSrcUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + CpuMathNativeUtils.ScaleSrcU(DEFAULT_SCALE, psrc, pdst, LEN); + } + } + + [Benchmark] + public void ManagedScaleSrcUPerf() => CpuMathUtils.Scale(DEFAULT_SCALE, src, dst, LEN); + + [Benchmark] + public unsafe void NativeScaleAddUPerf() + { + fixed (float* pdst = dst) + { + CpuMathNativeUtils.ScaleAddU(DEFAULT_SCALE, DEFAULT_SCALE, pdst, LEN); + } + } + + [Benchmark] + public void ManagedScaleAddUPerf() => CpuMathUtils.ScaleAdd(DEFAULT_SCALE, DEFAULT_SCALE, dst, LEN); + + [Benchmark] + public unsafe void NativeAddScaleUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + CpuMathNativeUtils.AddScaleU(DEFAULT_SCALE, psrc, pdst, LEN); + } + } + + [Benchmark] + public void ManagedAddScaleUPerf() => CpuMathUtils.AddScale(DEFAULT_SCALE, src, dst, LEN); + + [Benchmark] + public unsafe void NativeAddScaleSUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (int* pidx = idx) + { + CpuMathNativeUtils.AddScaleSU(DEFAULT_SCALE, psrc, pidx, pdst, IDXLEN); + } + } + + [Benchmark] + public void ManagedAddScaleSUPerf() => CpuMathUtils.AddScale(DEFAULT_SCALE, src, idx, dst, IDXLEN); + + [Benchmark] + public unsafe void NativeAddScaleCopyUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (float* pres = result) + { + CpuMathNativeUtils.AddScaleCopyU(DEFAULT_SCALE, psrc, pdst, pres, LEN); + } + } + + [Benchmark] + public void ManagedAddScaleCopyUPerf() => CpuMathUtils.AddScaleCopy(DEFAULT_SCALE, src, dst, result, LEN); + + [Benchmark] + public unsafe void NativeAddUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + CpuMathNativeUtils.AddU(psrc, pdst, LEN); + } + } + + [Benchmark] + public void ManagedAddUPerf() => CpuMathUtils.Add(src, dst, LEN); + + [Benchmark] + public unsafe void NativeAddSUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (int* pidx = idx) + { + CpuMathNativeUtils.AddSU(psrc, pidx, pdst, IDXLEN); + } + } + + [Benchmark] + public void ManagedAddSUPerf() => CpuMathUtils.Add(src, idx, dst, IDXLEN); + + + [Benchmark] + public unsafe void NativeMulElementWiseUPerf() + { + fixed (float* psrc1 = src1) + fixed (float* psrc2 = src2) + fixed (float* pdst = dst) + { + CpuMathNativeUtils.MulElementWiseU(psrc1, psrc2, pdst, LEN); + } + } + + [Benchmark] + public void ManagedMulElementWiseUPerf() => CpuMathUtils.MulElementWise(src1, src2, dst, LEN); + + [Benchmark] + public unsafe float NativeSumUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.SumU(psrc, LEN); + } + } + + [Benchmark] + public float ManagedSumUPerf() => CpuMathUtils.Sum(src, LEN); + + [Benchmark] + public unsafe float NativeSumSqUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.SumSqU(psrc, LEN); + } + } + + [Benchmark] + public float ManagedSumSqUPerf() => CpuMathUtils.SumSq(src, LEN); + + [Benchmark] + public unsafe float NativeSumSqDiffUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.SumSqDiffU(DEFAULT_SCALE, psrc, LEN); + } + } + + [Benchmark] + public float ManagedSumSqDiffUPerf() => CpuMathUtils.SumSq(DEFAULT_SCALE, src, 0, LEN); + + [Benchmark] + public unsafe float NativeSumAbsUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.SumAbsU(psrc, LEN); + } + } + + [Benchmark] + public float ManagedSumAbsUPerf() => CpuMathUtils.SumAbs(src, LEN); + + [Benchmark] + public unsafe float NativeSumAbsDiffUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.SumAbsDiffU(DEFAULT_SCALE, psrc, LEN); + } + } + + [Benchmark] + public float ManagedSumAbsDiffUPerf() => CpuMathUtils.SumAbs(DEFAULT_SCALE, src, 0, LEN); + + [Benchmark] + public unsafe float NativeMaxAbsUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.MaxAbsU(psrc, LEN); + } + } + + [Benchmark] + public float ManagedMaxAbsUPerf() => CpuMathUtils.MaxAbs(src, LEN); + + [Benchmark] + public unsafe float NativeMaxAbsDiffUPerf() + { + fixed (float* psrc = src) + { + return CpuMathNativeUtils.MaxAbsDiffU(DEFAULT_SCALE, psrc, LEN); + } + } + + [Benchmark] + public float ManagedMaxAbsDiffUPerf() => CpuMathUtils.MaxAbsDiff(DEFAULT_SCALE, src, LEN); + // TODO: MaxAbsU!!! + + [Benchmark] + public unsafe float NativeDotUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + return CpuMathNativeUtils.DotU(psrc, pdst, LEN); + } + } + + [Benchmark] + public float ManagedDotUPerf() => CpuMathUtils.DotProductDense(src, dst, LEN); + + [Benchmark] + public unsafe float NativeDotSUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (int* pidx = idx) + { + return CpuMathNativeUtils.DotSU(psrc, pdst, pidx, IDXLEN); + } + } + + [Benchmark] + public float ManagedDotSUPerf() => CpuMathUtils.DotProductSparse(src, dst, idx, IDXLEN); + + [Benchmark] + public unsafe float NativeDist2Perf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + { + return CpuMathNativeUtils.Dist2(psrc, pdst, LEN); + } + } + + [Benchmark] + public float ManagedDist2Perf() => CpuMathUtils.L2DistSquared(src, dst, LEN); + + [Benchmark] + public unsafe void NativeSdcaL1UpdateUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (float* pres = result) + { + CpuMathNativeUtils.SdcaL1UpdateU(DEFAULT_SCALE, psrc, DEFAULT_SCALE, pdst, pres, LEN); + } + } + + [Benchmark] + public void ManagedSdcaL1UpdateUPerf() => CpuMathUtils.SdcaL1UpdateDense(DEFAULT_SCALE, LEN, src, DEFAULT_SCALE, dst, result); + + [Benchmark] + public unsafe void NativeSdcaL1UpdateSUPerf() + { + fixed (float* psrc = src) + fixed (float* pdst = dst) + fixed (float* pres = result) + fixed (int* pidx = idx) + { + CpuMathNativeUtils.SdcaL1UpdateSU(DEFAULT_SCALE, psrc, pidx, DEFAULT_SCALE, pdst, pres, IDXLEN); + } + } + + [Benchmark] + public void ManagedSdcaL1UpdateSUPerf() => CpuMathUtils.SdcaL1UpdateSparse(DEFAULT_SCALE, LEN, src, idx, IDXLEN, DEFAULT_SCALE, dst, result); + } +} diff --git a/test/Microsoft.ML.CpuMath.UnitTests.netcoreapp/Microsoft.ML.CpuMath.UnitTests.netcoreapp.csproj b/test/Microsoft.ML.CpuMath.UnitTests.netcoreapp/Microsoft.ML.CpuMath.UnitTests.netcoreapp.csproj new file mode 100644 index 0000000000..e611b15032 --- /dev/null +++ b/test/Microsoft.ML.CpuMath.UnitTests.netcoreapp/Microsoft.ML.CpuMath.UnitTests.netcoreapp.csproj @@ -0,0 +1,16 @@ + + + + netcoreapp3.0 + false + + + + + + + + + + + diff --git a/test/Microsoft.ML.CpuMath.UnitTests.netstandard/Microsoft.ML.CpuMath.UnitTests.netstandard.csproj b/test/Microsoft.ML.CpuMath.UnitTests.netstandard/Microsoft.ML.CpuMath.UnitTests.netstandard.csproj new file mode 100644 index 0000000000..9552f688a8 --- /dev/null +++ b/test/Microsoft.ML.CpuMath.UnitTests.netstandard/Microsoft.ML.CpuMath.UnitTests.netstandard.csproj @@ -0,0 +1,16 @@ + + + + netcoreapp2.0 + false + + + + + + + + + + + diff --git a/test/Microsoft.ML.CpuMath.UnitTests.netstandard/UnitTests.cs b/test/Microsoft.ML.CpuMath.UnitTests.netstandard/UnitTests.cs new file mode 100644 index 0000000000..d1d5955a8e --- /dev/null +++ b/test/Microsoft.ML.CpuMath.UnitTests.netstandard/UnitTests.cs @@ -0,0 +1,618 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Xunit; +using Microsoft.ML.Runtime.Internal.CpuMath; + +namespace Microsoft.ML.CpuMath.UnitTests +{ + public class CpuMathUtilsUnitTests + { + private readonly float[][] testArrays; + private readonly int[] testIndexArray; + private readonly AlignedArray[] testMatrices; + private readonly AlignedArray[] testSrcVectors; + private readonly AlignedArray[] testDstVectors; + private const float DEFAULT_SCALE = 1.7f; + private const int SseCbAlign = 16; + private FloatEqualityComparer comparer; + + public CpuMathUtilsUnitTests() + { + // Padded array whose length is a multiple of 4 + float[] testArray1 = new float[8] { 1.96f, -2.38f, -9.76f, 13.84f, -106.37f, -26.93f, 32.45f, 3.29f }; + // Unpadded array whose length is not a multiple of 4. + float[] testArray2 = new float[7] { 1.96f, -2.38f, -9.76f, 13.84f, -106.37f, -26.93f, 32.45f }; + testArrays = new float[][] { testArray1, testArray2 }; + testIndexArray = new int[4] { 0, 2, 5, 6 }; + comparer = new FloatEqualityComparer(); + + // Padded matrices whose dimensions are multiples of 4 + float[] testMatrix1 = new float[4 * 4] { 1.96f, -2.38f, -9.76f, 13.84f, -106.37f, -26.93f, 32.45f, 3.29f, + 1.96f, -2.38f, -9.76f, 13.84f, -106.37f, -26.93f, 32.45f, 3.29f }; + float[] testMatrix2 = new float[4 * 8]; + + for (int i = 0; i < testMatrix2.Length; i++) + { + testMatrix2[i] = i + 1; + } + + AlignedArray testMatrixAligned1 = new AlignedArray(4 * 4, SseCbAlign); + AlignedArray testMatrixAligned2 = new AlignedArray(4 * 8, SseCbAlign); + testMatrixAligned1.CopyFrom(testMatrix1, 0, testMatrix1.Length); + testMatrixAligned2.CopyFrom(testMatrix2, 0, testMatrix2.Length); + + testMatrices = new AlignedArray[] { testMatrixAligned1, testMatrixAligned2 }; + + // Padded source vectors whose dimensions are multiples of 4 + float[] testSrcVector1 = new float[4] { 1f, 2f, 3f, 4f }; + float[] testSrcVector2 = new float[8] { 1f, 2f, 3f, 4f, 5f, 6f, 7f, 8f }; + + AlignedArray testSrcVectorAligned1 = new AlignedArray(4, SseCbAlign); + AlignedArray testSrcVectorAligned2 = new AlignedArray(8, SseCbAlign); + testSrcVectorAligned1.CopyFrom(testSrcVector1, 0, testSrcVector1.Length); + testSrcVectorAligned2.CopyFrom(testSrcVector2, 0, testSrcVector2.Length); + + testSrcVectors = new AlignedArray[] { testSrcVectorAligned1, testSrcVectorAligned2 }; + + // Padded destination vectors whose dimensions are multiples of 4 + float[] testDstVector1 = new float[4] { 0f, 1f, 2f, 3f }; + float[] testDstVector2 = new float[8] { 0f, 1f, 2f, 3f, 4f, 5f, 6f, 7f }; + + AlignedArray testDstVectorAligned1 = new AlignedArray(4, SseCbAlign); + AlignedArray testDstVectorAligned2 = new AlignedArray(8, SseCbAlign); + testDstVectorAligned1.CopyFrom(testDstVector1, 0, testDstVector1.Length); + testDstVectorAligned2.CopyFrom(testDstVector2, 0, testDstVector2.Length); + + testDstVectors = new AlignedArray[] { testDstVectorAligned1, testDstVectorAligned2 }; + } + + [Theory] + [InlineData(0, 0, 0, new float[] { 23.28f, -49.72f, 23.28f, -49.72f })] + [InlineData(1, 1, 0, new float[] { 204f, 492f, 780f, 1068f })] + [InlineData(1, 0, 1, new float[] { 30f, 70f, 110f, 150f, 190f, 230f, 270f, 310f })] + public void MatMulATest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + + CpuMathUtils.MatTimesSrc(false, false, mat, src, dst, dst.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { 23.28f, -48.72f, 25.28f, -46.72f })] + [InlineData(1, 1, 0, new float[] { 204f, 493f, 782f, 1071f })] + [InlineData(1, 0, 1, new float[] { 30f, 71f, 112f, 153f, 194f, 235f, 276f, 317f })] + public void MatMulAAddTest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + + CpuMathUtils.MatTimesSrc(false, true, mat, src, dst, dst.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { -630.38f, -171.1f, 155.66f, 75.1f })] + [InlineData(1, 0, 1, new float[] { 170f, 180f, 190f, 200f, 210f, 220f, 230f, 240f })] + [InlineData(1, 1, 0, new float[] { 708f, 744f, 780f, 816f })] + public void MatMulTranATest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + + CpuMathUtils.MatTimesSrc(true, false, mat, src, dst, src.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { -630.38f, -170.1f, 157.66f, 78.1f })] + [InlineData(1, 0, 1, new float[] { 170f, 181f, 192f, 203f, 214f, 225f, 236f, 247f })] + [InlineData(1, 1, 0, new float[] { 708f, 745f, 782f, 819f })] + public void MatMulTranAAddTest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + + CpuMathUtils.MatTimesSrc(true, true, mat, src, dst, src.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { -27.32f, -9.02f, -27.32f, -9.02f })] + [InlineData(1, 1, 0, new float[] { 95f, 231f, 367f, 503f })] + [InlineData(1, 0, 1, new float[] { 10f, 26f, 42f, 58f, 74f, 90f, 106f, 122f })] + public void MatMulPATest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + int[] idx = testIndexArray; + + CpuMathUtils.MatTimesSrc(false, false, mat, idx, src, 0, 0, 2 + 2 * srcTest, dst, dst.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { -27.32f, -8.02f, -25.32f, -6.02f })] + [InlineData(1, 1, 0, new float[] { 95f, 232f, 369f, 506f })] + [InlineData(1, 0, 1, new float[] { 10f, 27f, 44f, 61f, 78f, 95f, 112f, 129f })] + public void MatMulPAAddTest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + int[] idx = testIndexArray; + + CpuMathUtils.MatTimesSrc(false, true, mat, idx, src, 0, 0, 2 + 2 * srcTest, dst, dst.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { 7.84f, -9.52f, -39.04f, 55.36f })] + [InlineData(1, 0, 1, new float[] { 52f, 56f, 60f, 64f, 68f, 72f, 76f, 80f })] + [InlineData(1, 1, 0, new float[] { 329f, 346f, 363f, 380f })] + public void MatMulTranPATest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + int[] idx = testIndexArray; + + CpuMathUtils.MatTimesSrc(true, false, mat, idx, src, 0, 0, 2 + 2 * srcTest, dst, src.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, 0, 0, new float[] { 7.84f, -8.52f, -37.04f, 58.36f })] + [InlineData(1, 0, 1, new float[] { 52f, 57f, 62f, 67f, 72f, 77f, 82f, 87f })] + [InlineData(1, 1, 0, new float[] { 329f, 347f, 365f, 383f })] + public void MatMulTranPAAddTest(int matTest, int srcTest, int dstTest, float[] expected) + { + AlignedArray mat = testMatrices[matTest]; + AlignedArray src = testSrcVectors[srcTest]; + AlignedArray dst = testDstVectors[dstTest]; + int[] idx = testIndexArray; + + CpuMathUtils.MatTimesSrc(true, true, mat, idx, src, 0, 0, 2 + 2 * srcTest, dst, src.Size); + float[] actual = new float[dst.Size]; + dst.CopyTo(actual, 0, dst.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddScalarUTest(int test) + { + float[] dst = (float[])testArrays[test].Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] += DEFAULT_SCALE; + } + + CpuMathUtils.Add(DEFAULT_SCALE, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void ScaleUTest(int test) + { + float[] dst = (float[])testArrays[test].Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] *= DEFAULT_SCALE; + } + + CpuMathUtils.Scale(DEFAULT_SCALE, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void ScaleSrcUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] *= DEFAULT_SCALE; + } + + CpuMathUtils.Scale(DEFAULT_SCALE, src, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void ScaleAddUTest(int test) + { + float[] dst = (float[])testArrays[test].Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] = DEFAULT_SCALE * (dst[i] + DEFAULT_SCALE); + } + + CpuMathUtils.ScaleAdd(DEFAULT_SCALE, DEFAULT_SCALE, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddScaleUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] *= (1 + DEFAULT_SCALE); + } + + CpuMathUtils.AddScale(DEFAULT_SCALE, src, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddScaleSUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + int[] idx = testIndexArray; + float[] expected = (float[])dst.Clone(); + + expected[0] = 5.292f; + expected[2] = -13.806f; + expected[5] = -43.522f; + expected[6] = 55.978f; + + CpuMathUtils.AddScale(DEFAULT_SCALE, src, idx, dst, idx.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddScaleCopyUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + float[] result = (float[])dst.Clone(); + float[] expected = (float[])dst.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] *= (1 + DEFAULT_SCALE); + } + + CpuMathUtils.AddScaleCopy(DEFAULT_SCALE, src, dst, result, dst.Length); + var actual = result; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + float[] expected = (float[])src.Clone(); + + // Ensures src and dst are different arrays + for (int i = 0; i < dst.Length; i++) + { + dst[i] += 1; + } + + for (int i = 0; i < expected.Length; i++) + { + expected[i] = 2 * expected[i] + 1; + } + + CpuMathUtils.Add(src, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void AddSUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + int[] idx = testIndexArray; + float[] expected = (float[])dst.Clone(); + + expected[0] = 3.92f; + expected[2] = -12.14f; + expected[5] = -36.69f; + expected[6] = 46.29f; + + CpuMathUtils.Add(src, idx, dst, idx.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void MulElementWiseUTest(int test) + { + float[] src1 = (float[])testArrays[test].Clone(); + float[] src2 = (float[])src1.Clone(); + float[] dst = (float[])src1.Clone(); + + // Ensures src1 and src2 are different arrays + for (int i = 0; i < src2.Length; i++) + { + src2[i] += 1; + } + + float[] expected = (float[])src1.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + expected[i] *= (1 + expected[i]); + } + + CpuMathUtils.MulElementWise(src1, src2, dst, dst.Length); + var actual = dst; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, -93.9f)] + [InlineData(1, -97.19f)] + public void SumUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.Sum(src, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 13399.9376f)] + [InlineData(1, 13389.1135f)] + public void SumSqUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.SumSq(src, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 13742.3176f)] + [InlineData(1, 13739.7895f)] + public void SumSqDiffUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.SumSq(DEFAULT_SCALE, src, 0, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 196.98f)] + [InlineData(1, 193.69f)] + public void SumAbsUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.SumAbs(src, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 196.98f)] + [InlineData(1, 195.39f)] + public void SumAbsDiffUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.SumAbs(DEFAULT_SCALE, src, 0, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 106.37f)] + [InlineData(1, 106.37f)] + public void MaxAbsUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.MaxAbs(src, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 108.07f)] + [InlineData(1, 108.07f)] + public void MaxAbsDiffUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + var actual = CpuMathUtils.MaxAbsDiff(DEFAULT_SCALE, src, src.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 13306.0376f)] + [InlineData(1, 13291.9235f)] + public void DotUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + + for (int i = 0; i < dst.Length; i++) + { + dst[i] += 1; + } + + var actual = CpuMathUtils.DotProductDense(src, dst, dst.Length); + Assert.Equal(expected, actual, 2); + } + + [Theory] + [InlineData(0, 736.7352f)] + [InlineData(1, 736.7352f)] + public void DotSUTest(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + int[] idx = testIndexArray; + + // Ensures src and dst are different arrays + for (int i = 0; i < dst.Length; i++) + { + dst[i] += 1; + } + + var actual = CpuMathUtils.DotProductSparse(src, dst, idx, idx.Length); + Assert.Equal(expected, actual, 4); + } + + [Theory] + [InlineData(0, 8.0f)] + [InlineData(1, 7.0f)] + public void Dist2Test(int test, float expected) + { + float[] src = (float[])testArrays[test].Clone(); + float[] dst = (float[])src.Clone(); + + // Ensures src and dst are different arrays + for (int i = 0; i < dst.Length; i++) + { + dst[i] += 1; + } + + var actual = CpuMathUtils.L2DistSquared(src, dst, dst.Length); + Assert.Equal(expected, actual, 0); + } + + [Theory] + [InlineData(0, new int[] { 0, 2 }, new float[] { 0f, 2f, 0f, 4f })] + [InlineData(1, new int[] { 0, 2, 5, 6 }, new float[] { 0f, 2f, 0f, 4f, 5f, 0f, 0f, 8f })] + public void ZeroItemsUTest(int test, int[] idx, float[] expected) + { + AlignedArray src = new AlignedArray(4 + 4 * test, SseCbAlign); + src.CopyFrom(testSrcVectors[test]); + + CpuMathUtils.ZeroMatrixItems(src, src.Size, src.Size, idx); + float[] actual = new float[src.Size]; + src.CopyTo(actual, 0, src.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0, new int[] { 0, 1 }, new float[] { 0f, 2f, 0f, 4f })] + [InlineData(1, new int[] { 0, 2, 4 }, new float[] { 0f, 2f, 0f, 4f, 5f, 0f, 7f, 8f })] + public void ZeroMatrixItemsCoreTest(int test, int[] idx, float[] expected) + { + AlignedArray src = new AlignedArray(4 + 4 * test, SseCbAlign); + src.CopyFrom(testSrcVectors[test]); + + CpuMathUtils.ZeroMatrixItems(src, src.Size / 2 - 1, src.Size / 2, idx); + float[] actual = new float[src.Size]; + src.CopyTo(actual, 0, src.Size); + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void SdcaL1UpdateUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] v = (float[])src.Clone(); + float[] w = (float[])src.Clone(); + float[] expected = (float[])w.Clone(); + + for (int i = 0; i < expected.Length; i++) + { + float value = src[i] * (1 + DEFAULT_SCALE); + expected[i] = Math.Abs(value) > DEFAULT_SCALE ? (value > 0 ? value - DEFAULT_SCALE : value + DEFAULT_SCALE) : 0; + } + + CpuMathUtils.SdcaL1UpdateDense(DEFAULT_SCALE, src.Length, src, DEFAULT_SCALE, v, w); + var actual = w; + Assert.Equal(expected, actual, comparer); + } + + [Theory] + [InlineData(0)] + [InlineData(1)] + public void SdcaL1UpdateSUTest(int test) + { + float[] src = (float[])testArrays[test].Clone(); + float[] v = (float[])src.Clone(); + float[] w = (float[])src.Clone(); + int[] idx = testIndexArray; + float[] expected = (float[])w.Clone(); + + for (int i = 0; i < idx.Length; i++) + { + int index = idx[i]; + float value = v[index] + src[i] * DEFAULT_SCALE; + expected[index] = Math.Abs(value) > DEFAULT_SCALE ? (value > 0 ? value - DEFAULT_SCALE : value + DEFAULT_SCALE) : 0; + } + + CpuMathUtils.SdcaL1UpdateSparse(DEFAULT_SCALE, src.Length, src, idx, idx.Length, DEFAULT_SCALE, v, w); + var actual = w; + Assert.Equal(expected, actual, comparer); + } + } + + internal class FloatEqualityComparer : IEqualityComparer + { + public bool Equals(float a, float b) + { + return Math.Abs(a - b) < 1e-5f; + } + + public int GetHashCode(float a) + { + throw new NotImplementedException(); + } + } +} diff --git a/test/Microsoft.ML.FSharp.Tests/Microsoft.ML.FSharp.Tests.fsproj b/test/Microsoft.ML.FSharp.Tests/Microsoft.ML.FSharp.Tests.fsproj new file mode 100644 index 0000000000..888a983e51 --- /dev/null +++ b/test/Microsoft.ML.FSharp.Tests/Microsoft.ML.FSharp.Tests.fsproj @@ -0,0 +1,52 @@ + + + + netcoreapp2.0 + $(TargetFrameworks); net461 + 2003;$(NoWarn) + false + + x64 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/test/Microsoft.ML.FSharp.Tests/Program.fs b/test/Microsoft.ML.FSharp.Tests/Program.fs new file mode 100644 index 0000000000..f45e4e3c6c --- /dev/null +++ b/test/Microsoft.ML.FSharp.Tests/Program.fs @@ -0,0 +1,9 @@ +namespace Microsoft.ML.FSharp.Tests + +#if NETCOREAPP2_0 +module Program = + + [] + let main _ = 0 +#endif + diff --git a/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs b/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs new file mode 100644 index 0000000000..570c1e0722 --- /dev/null +++ b/test/Microsoft.ML.FSharp.Tests/SmokeTests.fs @@ -0,0 +1,263 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + + +//================================================================================================= +// This test can be run either as a compiled test with .NET Core (on any platform) or +// manually in script form (to help debug it and also check that F# scripting works with ML.NET). +// Running as a script requires using F# Interactive on Windows, and the explicit references below. +// The references would normally be created by a package loader for the scripting +// environment, e.g. see https://github.com/isaacabraham/ml-test-experiment/, but +// here we list them explicitly to avoid the dependency on a package loader, +// +// You should build Microsoft.ML.FSharp.Tests in Debug mode for framework net461 +// before running this as a script with F# Interactive by editing the project +// file to have: +// netcoreapp2.0; net461 + +#if INTERACTIVE +#r "netstandard" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.Core.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Google.Protobuf.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Newtonsoft.Json.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/System.CodeDom.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/System.Threading.Tasks.Dataflow.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.CpuMath.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.Data.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.Transforms.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.ResultProcessor.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.PCA.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.KMeansClustering.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.FastTree.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.Api.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.Sweeper.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.StandardLearners.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/Microsoft.ML.PipelineInference.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/xunit.core.dll" +#r @"../../bin/AnyCPU.Debug/Microsoft.ML.FSharp.Tests/net461/xunit.assert.dll" +#r "System" +#r "System.ComponentModel.Composition" +#r "System.Core" +#r "System.Xml.Linq" + +// Later tests will add data import using F# type providers: +//#r @"../../packages/fsharp.data/3.0.0-beta4/lib/netstandard2.0/FSharp.Data.dll" // this must be referenced from its package location + +#endif + +//================================================================================ +// The tests proper start here + +#if !INTERACTIVE +namespace Microsoft.ML.FSharp.Tests +#endif + +open System +open Microsoft.ML +open Microsoft.ML.Data +open Microsoft.ML.Transforms +open Microsoft.ML.Trainers +open Microsoft.ML.Runtime.Api +open Xunit + +module SmokeTest1 = + + type SentimentData() = + [] + val mutable SentimentText : string + [] + val mutable Sentiment : float32 + + type SentimentPrediction() = + [] + val mutable Sentiment : bool + + [] + let ``FSharp-Sentiment-Smoke-Test`` () = + + // See https://github.com/dotnet/machinelearning/issues/401: forces the loading of ML.NET component assemblies + let _load = + [ typeof; + typeof ] + + let testDataPath = __SOURCE_DIRECTORY__ + @"/../data/wikipedia-detox-250-line-data.tsv" + + let pipeline = LearningPipeline() + + pipeline.Add( + TextLoader(testDataPath).CreateFrom( + Arguments = + TextLoaderArguments( + HasHeader = true, + Column = [| TextLoaderColumn(Name = "Label", + Source = [| TextLoaderRange(0) |], + Type = Nullable (Data.DataKind.Num)) + TextLoaderColumn(Name = "SentimentText", + Source = [| TextLoaderRange(1) |], + Type = Nullable (Data.DataKind.Text)) |] + ))) + + pipeline.Add( + TextFeaturizer( + "Features", [| "SentimentText" |], + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = TextNormalizerTransformCaseNormalizationMode.Lower, + OutputTokens = true, + VectorNormalizer = TextTransformTextNormKind.L2 + )) + + pipeline.Add( + FastTreeBinaryClassifier( + NumLeaves = 5, + NumTrees = 5, + MinDocumentsInLeafs = 2 + )) + + let model = pipeline.Train() + + let predictions = + [ SentimentData(SentimentText = "This is a gross exaggeration. Nobody is setting a kangaroo court. There was a simple addition.") + SentimentData(SentimentText = "Sort of ok") + SentimentData(SentimentText = "Joe versus the Volcano Coffee Company is a great film.") ] + |> model.Predict + + let predictionResults = [ for p in predictions -> p.Sentiment ] + Assert.Equal(predictionResults, [ false; true; true ]) + +module SmokeTest2 = + + [] + type SentimentData = + { [] + SentimentText : string + + [] + Sentiment : float32 } + + [] + type SentimentPrediction = + { [] + Sentiment : bool } + + [] + let ``FSharp-Sentiment-Smoke-Test`` () = + + // See https://github.com/dotnet/machinelearning/issues/401: forces the loading of ML.NET component assemblies + let _load = + [ typeof; + typeof ] + + let testDataPath = __SOURCE_DIRECTORY__ + @"/../data/wikipedia-detox-250-line-data.tsv" + + let pipeline = LearningPipeline() + + pipeline.Add( + TextLoader(testDataPath).CreateFrom( + Arguments = + TextLoaderArguments( + HasHeader = true, + Column = [| TextLoaderColumn(Name = "Label", + Source = [| TextLoaderRange(0) |], + Type = Nullable (Data.DataKind.Num)) + TextLoaderColumn(Name = "SentimentText", + Source = [| TextLoaderRange(1) |], + Type = Nullable (Data.DataKind.Text)) |] + ))) + + pipeline.Add( + TextFeaturizer( + "Features", [| "SentimentText" |], + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = TextNormalizerTransformCaseNormalizationMode.Lower, + OutputTokens = true, + VectorNormalizer = TextTransformTextNormKind.L2 + )) + + pipeline.Add( + FastTreeBinaryClassifier( + NumLeaves = 5, + NumTrees = 5, + MinDocumentsInLeafs = 2 + )) + + let model = pipeline.Train() + + let predictions = + [ { SentimentText = "This is a gross exaggeration. Nobody is setting a kangaroo court. There was a simple addition."; Sentiment = 0.0f } + { SentimentText = "Sort of ok"; Sentiment = 0.0f } + { SentimentText = "Joe versus the Volcano Coffee Company is a great film."; Sentiment = 0.0f } ] + |> model.Predict + + let predictionResults = [ for p in predictions -> p.Sentiment ] + Assert.Equal(predictionResults, [ false; true; true ]) + +module SmokeTest3 = + + type SentimentData() = + [] + member val SentimentText = "" with get, set + + [] + member val Sentiment = 0.0 with get, set + + type SentimentPrediction() = + [] + member val Sentiment = false with get, set + + [] + let ``FSharp-Sentiment-Smoke-Test`` () = + + // See https://github.com/dotnet/machinelearning/issues/401: forces the loading of ML.NET component assemblies + let _load = + [ typeof; + typeof ] + + let testDataPath = __SOURCE_DIRECTORY__ + @"/../data/wikipedia-detox-250-line-data.tsv" + + let pipeline = LearningPipeline() + + pipeline.Add( + TextLoader(testDataPath).CreateFrom( + Arguments = + TextLoaderArguments( + HasHeader = true, + Column = [| TextLoaderColumn(Name = "Label", + Source = [| TextLoaderRange(0) |], + Type = Nullable (Data.DataKind.Num)) + TextLoaderColumn(Name = "SentimentText", + Source = [| TextLoaderRange(1) |], + Type = Nullable (Data.DataKind.Text)) |] + ))) + + pipeline.Add( + TextFeaturizer( + "Features", [| "SentimentText" |], + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = TextNormalizerTransformCaseNormalizationMode.Lower, + OutputTokens = true, + VectorNormalizer = TextTransformTextNormKind.L2 + )) + + pipeline.Add( + FastTreeBinaryClassifier( + NumLeaves = 5, + NumTrees = 5, + MinDocumentsInLeafs = 2 + )) + + let model = pipeline.Train() + + let predictions = + [ SentimentData(SentimentText = "This is a gross exaggeration. Nobody is setting a kangaroo court. There was a simple addition.") + SentimentData(SentimentText = "Sort of ok") + SentimentData(SentimentText = "Joe versus the Volcano Coffee Company is a great film.") ] + |> model.Predict + + let predictionResults = [ for p in predictions -> p.Sentiment ] + Assert.Equal(predictionResults, [ false; true; true ]) + diff --git a/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj b/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj index 7f0a8d05f9..738d3ebebe 100644 --- a/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj +++ b/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj @@ -9,6 +9,7 @@ + @@ -27,6 +28,8 @@ + + diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 7f6c4b5ccd..059443c57d 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -231,6 +231,18 @@ public void BinaryClassifierLogisticRegressionTest() Done(); } + [Fact] + [TestCategory("Binary")] + public void BinaryClassifierSymSgdTest() + { + //Results sometimes go out of error tolerance on OS X. + if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) + return; + + RunOneAllTests(TestLearners.symSGD, TestDatasets.breastCancer, summary: true); + Done(); + } + [Fact] [TestCategory("Binary")] public void BinaryClassifierTesterThresholdingTest() @@ -888,11 +900,11 @@ public void RegressorOlsTest() /// /// A test for ordinary least squares regression. /// - [Fact(Skip = "Need CoreTLC specific baseline update")] + [Fact] [TestCategory("Regressor")] public void RegressorOlsTestOne() { - Run_TrainTest(TestLearners.Ols, TestDatasets.housing); + Run_TrainTest(TestLearners.Ols, TestDatasets.winequality); Done(); } @@ -905,8 +917,7 @@ public void RegressorOlsTestOne() public void RegressorSdcaTest() { var regressionPredictors = new[] { TestLearners.Sdcar, TestLearners.SdcarNorm, TestLearners.SdcarReg }; - var regressionDatasets = GetDatasetsForRegressorTest(); - RunAllTests(regressionPredictors, regressionDatasets); + RunAllTests(regressionPredictors, new[] { TestDatasets.winequality }); Done(); } diff --git a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs index 29da956c62..b53062c1a8 100644 --- a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs +++ b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipeBase.cs @@ -8,6 +8,7 @@ using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; using Xunit; @@ -215,28 +216,35 @@ protected void VerifyArgParsing(string[] strs) VerifyCustArgs(kvp.Value); } - protected void VerifyCustArgs(SubComponent sub) + protected void VerifyCustArgs(IComponentFactory factory) where TRes : class { - var str = CmdParser.CombineSettings(sub.Settings); - var info = ComponentCatalog.GetLoadableClassInfo(sub.Kind); - Assert.NotNull(info); - var def = info.CreateArguments(); + if (factory is ICommandLineComponentFactory commandLineFactory) + { + var str = commandLineFactory.GetSettingsString(); + var info = ComponentCatalog.GetLoadableClassInfo(commandLineFactory.Name, commandLineFactory.SignatureType); + Assert.NotNull(info); + var def = info.CreateArguments(); - var a1 = info.CreateArguments(); - CmdParser.ParseArguments(Env, str, a1); + var a1 = info.CreateArguments(); + CmdParser.ParseArguments(Env, str, a1); - // Get both the expanded and custom forms. - string exp1 = CmdParser.GetSettings(Env, a1, def, SettingsFlags.Default | SettingsFlags.NoUnparse); - string cust = CmdParser.GetSettings(Env, a1, def); + // Get both the expanded and custom forms. + string exp1 = CmdParser.GetSettings(Env, a1, def, SettingsFlags.Default | SettingsFlags.NoUnparse); + string cust = CmdParser.GetSettings(Env, a1, def); - // Map cust back to an object, then get its full form. - var a2 = info.CreateArguments(); - CmdParser.ParseArguments(Env, cust, a2); - string exp2 = CmdParser.GetSettings(Env, a2, def, SettingsFlags.Default | SettingsFlags.NoUnparse); + // Map cust back to an object, then get its full form. + var a2 = info.CreateArguments(); + CmdParser.ParseArguments(Env, cust, a2); + string exp2 = CmdParser.GetSettings(Env, a2, def, SettingsFlags.Default | SettingsFlags.NoUnparse); - if (exp1 != exp2) - Fail("Custom unparse failed on '{0}' starting with '{1}': '{2}' vs '{3}'", sub.Kind, str, exp1, exp2); + if (exp1 != exp2) + Fail("Custom unparse failed on '{0}' starting with '{1}': '{2}' vs '{3}'", commandLineFactory.Name, str, exp1, exp2); + } + else + { + Fail($"TestDataPipeBase was called with a non command line loader or transform '{factory}'"); + } } protected bool SaveLoadText(IDataView view, IHostEnvironment env, diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 9672c88fe8..4eaad61355 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -163,6 +163,14 @@ static TestLearnersBase() BaselineProgress = true }; + // New. + public static PredictorAndArgs symSGD = new PredictorAndArgs + { + Trainer = new SubComponent("SymSGD", "nt=1"), + MamlArgs = new[] { "norm=no" }, + BaselineProgress = true + }; + // New. public static PredictorAndArgs logisticRegressionNonNegative = new PredictorAndArgs { diff --git a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs index 87e23952d6..14a7f473f7 100644 --- a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs +++ b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs @@ -174,6 +174,49 @@ public void CanTrain() } + [Fact] + public void CanTrainProperties() + { + var pipeline = new LearningPipeline(); + var data = new List() { + new IrisDataProperties { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1}, + new IrisDataProperties { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1}, + new IrisDataProperties { SepalLength = 1.2f, SepalWidth = 0.5f, PetalLength=0.3f, PetalWidth=5.1f, Label=0} + }; + var collection = CollectionDataSource.Create(data); + + pipeline.Add(collection); + pipeline.Add(new ColumnConcatenator(outputColumn: "Features", + "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")); + pipeline.Add(new StochasticDualCoordinateAscentClassifier()); + PredictionModel model = pipeline.Train(); + + IrisPredictionProperties prediction = model.Predict(new IrisDataProperties() + { + SepalLength = 3.3f, + SepalWidth = 1.6f, + PetalLength = 0.2f, + PetalWidth = 5.1f, + }); + + pipeline = new LearningPipeline(); + collection = CollectionDataSource.Create(data.AsEnumerable()); + pipeline.Add(collection); + pipeline.Add(new ColumnConcatenator(outputColumn: "Features", + "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")); + pipeline.Add(new StochasticDualCoordinateAscentClassifier()); + model = pipeline.Train(); + + prediction = model.Predict(new IrisDataProperties() + { + SepalLength = 3.3f, + SepalWidth = 1.6f, + PetalLength = 0.2f, + PetalWidth = 5.1f, + }); + + } + public class Input { [Column("0")] @@ -207,6 +250,37 @@ public class IrisPrediction public float[] PredictedLabels; } + public class IrisDataProperties + { + private float _Label; + private float _SepalLength; + private float _SepalWidth; + private float _PetalLength; + private float _PetalWidth; + + [Column("0")] + public float Label { get { return _Label; } set { _Label = value; } } + + [Column("1")] + public float SepalLength { get { return _SepalLength; } set { _SepalLength = value; } } + + [Column("2")] + public float SepalWidth { get { return _SepalWidth; } set { _SepalWidth = value; } } + + [Column("3")] + public float PetalLength { get { return _PetalLength; } set { _PetalLength = value; } } + + [Column("4")] + public float PetalWidth { get { return _PetalWidth; } set { _PetalWidth = value; } } + } + + public class IrisPredictionProperties + { + private float[] _PredictedLabels; + [ColumnName("Score")] + public float[] PredictedLabels { get { return _PredictedLabels; } set { _PredictedLabels = value; } } + } + public class ConversionSimpleClass { public int fInt; @@ -257,7 +331,7 @@ public bool CompareObjectValues(object x, object y, Type type) public bool CompareThroughReflection(T x, T y) { - foreach (var field in typeof(T).GetFields()) + foreach (var field in typeof(T).GetFields(BindingFlags.Public | BindingFlags.Instance)) { var xvalue = field.GetValue(x); var yvalue = field.GetValue(y); @@ -272,6 +346,25 @@ public bool CompareThroughReflection(T x, T y) return false; } } + foreach (var property in typeof(T).GetProperties(BindingFlags.Public | BindingFlags.Instance)) + { + // Don't compare properties with private getters and setters + if (!property.CanRead || !property.CanWrite || property.GetGetMethod() == null || property.GetSetMethod() == null) + continue; + + var xvalue = property.GetValue(x); + var yvalue = property.GetValue(y); + if (property.PropertyType.IsArray) + { + if (!CompareArrayValues(xvalue as Array, yvalue as Array)) + return false; + } + else + { + if (!CompareObjectValues(xvalue, yvalue, property.PropertyType)) + return false; + } + } return true; } @@ -288,14 +381,6 @@ public bool CompareArrayValues(Array x, Array y) return true; } - public class ClassWithConstField - { - public const string ConstString = "N"; - public string fString; - public const int ConstInt = 100; - public int fInt; - } - [Fact] public void RoundTripConversionWithBasicTypes() { @@ -489,6 +574,50 @@ public void ConversionMinValueToNullBehavior() } } + public class ConversionLossMinValueClassProperties + { + private int? _fInt; + private long? _fLong; + private short? _fShort; + private sbyte? _fsByte; + public int? IntProp { get { return _fInt; } set { _fInt = value; } } + public short? ShortProp { get { return _fShort; } set { _fShort = value; } } + public sbyte? SByteProp { get { return _fsByte; } set { _fsByte = value; } } + public long? LongProp { get { return _fLong; } set { _fLong = value; } } + } + + [Fact] + public void ConversionMinValueToNullBehaviorProperties() + { + using (var env = new TlcEnvironment()) + { + + var data = new List + { + new ConversionLossMinValueClassProperties() { SByteProp = null, IntProp = null, LongProp = null, ShortProp = null }, + new ConversionLossMinValueClassProperties() { SByteProp = sbyte.MinValue, IntProp = int.MinValue, LongProp = long.MinValue, ShortProp = short.MinValue } + }; + foreach (var field in typeof(ConversionLossMinValueClassProperties).GetFields()) + { + var dataView = ComponentCreation.CreateDataView(env, data); + var enumerator = dataView.AsEnumerable(env, false).GetEnumerator(); + while (enumerator.MoveNext()) + { + Assert.True(enumerator.Current.IntProp == null && enumerator.Current.LongProp == null && + enumerator.Current.SByteProp == null && enumerator.Current.ShortProp == null); + } + } + } + } + + public class ClassWithConstField + { + public const string ConstString = "N"; + public string fString; + public const int ConstInt = 100; + public int fInt; + } + [Fact] public void ClassWithConstFieldsConversion() { @@ -510,6 +639,122 @@ public void ClassWithConstFieldsConversion() } } + + public class ClassWithMixOfFieldsAndProperties + { + public string fString; + private int _fInt; + public int IntProp { get { return _fInt; } set { _fInt = value; } } + } + + [Fact] + public void ClassWithMixOfFieldsAndPropertiesConversion() + { + var data = new List() + { + new ClassWithMixOfFieldsAndProperties(){ IntProp=1, fString ="lala" }, + new ClassWithMixOfFieldsAndProperties(){ IntProp=-1, fString ="" }, + new ClassWithMixOfFieldsAndProperties(){ IntProp=0, fString =null } + }; + + using (var env = new TlcEnvironment()) + { + var dataView = ComponentCreation.CreateDataView(env, data); + var enumeratorSimple = dataView.AsEnumerable(env, false).GetEnumerator(); + var originalEnumerator = data.GetEnumerator(); + while (enumeratorSimple.MoveNext() && originalEnumerator.MoveNext()) + Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); + Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); + } + } + + public abstract class BaseClassWithInheritedProperties + { + private string _fString; + private byte _fByte; + public string StringProp { get { return _fString; } set { _fString = value; } } + public abstract long LongProp { get; set; } + public virtual byte ByteProp { get { return _fByte; } set { _fByte = value; } } + } + + + public class ClassWithPrivateFieldsAndProperties + { + public ClassWithPrivateFieldsAndProperties() { seq++; _unusedStaticField++; _unusedPrivateField1 = 100; } + static public int seq; + static public int _unusedStaticField; + private int _unusedPrivateField1; + private string _fString; + + // This property is ignored because it has no setter + private int UnusedReadOnlyProperty { get { return _unusedPrivateField1; } } + + // This property is ignored because it is private + private int UnusedPrivateProperty { get { return _unusedPrivateField1; } set { _unusedPrivateField1 = value; } } + + // This property is ignored because it has a private setter + public int UnusedPropertyWithPrivateSetter { get { return _unusedPrivateField1; } private set { _unusedPrivateField1 = value; } } + + // This property is ignored because it has a private getter + public int UnusedPropertyWithPrivateGetter { private get { return _unusedPrivateField1; } set { _unusedPrivateField1 = value; } } + + public string StringProp { get { return _fString; } set { _fString = value; } } + } + + [Fact] + public void ClassWithPrivateFieldsAndPropertiesConversion() + { + var data = new List() + { + new ClassWithPrivateFieldsAndProperties(){ StringProp ="lala" }, + new ClassWithPrivateFieldsAndProperties(){ StringProp ="baba" } + }; + + using (var env = new TlcEnvironment()) + { + var dataView = ComponentCreation.CreateDataView(env, data); + var enumeratorSimple = dataView.AsEnumerable(env, false).GetEnumerator(); + var originalEnumerator = data.GetEnumerator(); + while (enumeratorSimple.MoveNext() && originalEnumerator.MoveNext()) + { + Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); + Assert.True(enumeratorSimple.Current.UnusedPropertyWithPrivateSetter == 100); + } + Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); + } + } + + public class ClassWithInheritedProperties : BaseClassWithInheritedProperties + { + private int _fInt; + private long _fLong; + private byte _fByte2; + public int IntProp { get { return _fInt; } set { _fInt = value; } } + public override long LongProp { get { return _fLong; } set { _fLong = value; } } + public override byte ByteProp { get { return _fByte2; } set { _fByte2 = value; } } + } + + [Fact] + public void ClassWithInheritedPropertiesConversion() + { + var data = new List() + { + new ClassWithInheritedProperties(){ IntProp=1, StringProp ="lala", LongProp=17, ByteProp=3 }, + new ClassWithInheritedProperties(){ IntProp=-1, StringProp ="", LongProp=2, ByteProp=4 }, + new ClassWithInheritedProperties(){ IntProp=0, StringProp =null, LongProp=18, ByteProp=5 } + }; + + using (var env = new TlcEnvironment()) + { + var dataView = ComponentCreation.CreateDataView(env, data); + var enumeratorSimple = dataView.AsEnumerable(env, false).GetEnumerator(); + var originalEnumerator = data.GetEnumerator(); + while (enumeratorSimple.MoveNext() && originalEnumerator.MoveNext()) + Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); + Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); + } + } + public class ClassWithArrays { public string[] fString; @@ -609,5 +854,129 @@ public void RoundTripConversionWithArrays() Assert.True(!enumeratorNullable.MoveNext() && !originalNullalbleEnumerator.MoveNext()); } } + public class ClassWithArrayProperties + { + private string[] _fString; + private int[] _fInt; + private uint[] _fuInt; + private short[] _fShort; + private ushort[] _fuShort; + private sbyte[] _fsByte; + private byte[] _fByte; + private long[] _fLong; + private ulong[] _fuLong; + private float[] _fFloat; + private double[] _fDouble; + private bool[] _fBool; + public string[] StringProp { get { return _fString; } set { _fString = value; } } + public int[] IntProp { get { return _fInt; } set { _fInt = value; } } + public uint[] UIntProp { get { return _fuInt; } set { _fuInt = value; } } + public short[] ShortProp { get { return _fShort; } set { _fShort = value; } } + public ushort[] UShortProp { get { return _fuShort; } set { _fuShort = value; } } + public sbyte[] SByteProp { get { return _fsByte; } set { _fsByte = value; } } + public byte[] ByteProp { get { return _fByte; } set { _fByte = value; } } + public long[] LongProp { get { return _fLong; } set { _fLong = value; } } + public ulong[] ULongProp { get { return _fuLong; } set { _fuLong = value; } } + public float[] FloatProp { get { return _fFloat; } set { _fFloat = value; } } + public double[] DobuleProp { get { return _fDouble; } set { _fDouble = value; } } + public bool[] BoolProp { get { return _fBool; } set { _fBool = value; } } + } + + public class ClassWithNullableArrayProperties + { + private string[] _fString; + private int?[] _fInt; + private uint?[] _fuInt; + private short?[] _fShort; + private ushort?[] _fuShort; + private sbyte?[] _fsByte; + private byte?[] _fByte; + private long?[] _fLong; + private ulong?[] _fuLong; + private float?[] _fFloat; + private double?[] _fDouble; + private bool?[] _fBool; + + public string[] StringProp { get { return _fString; } set { _fString = value; } } + public int?[] IntProp { get { return _fInt; } set { _fInt = value; } } + public uint?[] UIntProp { get { return _fuInt; } set { _fuInt = value; } } + public short?[] ShortProp { get { return _fShort; } set { _fShort = value; } } + public ushort?[] UShortProp { get { return _fuShort; } set { _fuShort = value; } } + public sbyte?[] SByteProp { get { return _fsByte; } set { _fsByte = value; } } + public byte?[] ByteProp { get { return _fByte; } set { _fByte = value; } } + public long?[] LongProp { get { return _fLong; } set { _fLong = value; } } + public ulong?[] ULongProp { get { return _fuLong; } set { _fuLong = value; } } + public float?[] SingleProp { get { return _fFloat; } set { _fFloat = value; } } + public double?[] DoubleProp { get { return _fDouble; } set { _fDouble = value; } } + public bool?[] BoolProp { get { return _fBool; } set { _fBool = value; } } + } + + [Fact] + public void RoundTripConversionWithArrayPropertiess() + { + + var data = new List + { + new ClassWithArrayProperties() + { + IntProp = new int[3] { 0, 1, 2 }, + FloatProp = new float[3] { -0.99f, 0f, 0.99f }, + StringProp = new string[2] { "hola", "lola" }, + BoolProp = new bool[2] { true, false }, + ByteProp = new byte[3] { 0, 124, 255 }, + DobuleProp = new double[3] { -1, 0, 1 }, + LongProp = new long[] { 0, 1, 2 }, + SByteProp = new sbyte[3] { -127, 127, 0 }, + ShortProp = new short[3] { 0, 1225, 32767 }, + UIntProp = new uint[2] { 0, uint.MaxValue }, + ULongProp = new ulong[2] { ulong.MaxValue, 0 }, + UShortProp = new ushort[2] { 0, ushort.MaxValue } + }, + new ClassWithArrayProperties() { IntProp = new int[3] { -2, 1, 0 }, FloatProp = new float[3] { 0.99f, 0f, -0.99f }, StringProp = new string[2] { "", null } }, + new ClassWithArrayProperties() + }; + + var nullableData = new List + { + new ClassWithNullableArrayProperties() + { + IntProp = new int?[3] { null, -1, 1 }, + SingleProp = new float?[3] { -0.99f, null, 0.99f }, + StringProp = new string[2] { null, "" }, + BoolProp = new bool?[3] { true, null, false }, + ByteProp = new byte?[4] { 0, 125, null, 255 }, + DoubleProp = new double?[3] { -1, null, 1 }, + LongProp = new long?[] { null, -1, 1 }, + SByteProp = new sbyte?[3] { -127, 127, null }, + ShortProp = new short?[3] { 0, null, 32767 }, + UIntProp = new uint?[4] { null, 42, 0, uint.MaxValue }, + ULongProp = new ulong?[3] { ulong.MaxValue, null, 0 }, + UShortProp = new ushort?[3] { 0, null, ushort.MaxValue } + }, + new ClassWithNullableArrayProperties() { IntProp = new int?[3] { -2, 1, 0 }, SingleProp = new float?[3] { 0.99f, 0f, -0.99f }, StringProp = new string[2] { "lola", "hola" } }, + new ClassWithNullableArrayProperties() + }; + + using (var env = new TlcEnvironment()) + { + var dataView = ComponentCreation.CreateDataView(env, data); + var enumeratorSimple = dataView.AsEnumerable(env, false).GetEnumerator(); + var originalEnumerator = data.GetEnumerator(); + while (enumeratorSimple.MoveNext() && originalEnumerator.MoveNext()) + { + Assert.True(CompareThroughReflection(enumeratorSimple.Current, originalEnumerator.Current)); + } + Assert.True(!enumeratorSimple.MoveNext() && !originalEnumerator.MoveNext()); + + var nullableDataView = ComponentCreation.CreateDataView(env, nullableData); + var enumeratorNullable = nullableDataView.AsEnumerable(env, false).GetEnumerator(); + var originalNullalbleEnumerator = nullableData.GetEnumerator(); + while (enumeratorNullable.MoveNext() && originalNullalbleEnumerator.MoveNext()) + { + Assert.True(CompareThroughReflection(enumeratorNullable.Current, originalNullalbleEnumerator.Current)); + } + Assert.True(!enumeratorNullable.MoveNext() && !originalNullalbleEnumerator.MoveNext()); + } + } } } diff --git a/test/Microsoft.ML.Tests/ImagesTests.cs b/test/Microsoft.ML.Tests/ImagesTests.cs new file mode 100644 index 0000000000..a12032400a --- /dev/null +++ b/test/Microsoft.ML.Tests/ImagesTests.cs @@ -0,0 +1,183 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.ImageAnalytics; +using Microsoft.ML.TestFramework; +using System.Drawing; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class ImageTests : BaseTestClass + { + public ImageTests(ITestOutputHelper output) : base(output) + { + } + + [Fact] + public void TestSaveImages() + { + using (var env = new TlcEnvironment()) + { + var dataFile = GetDataPath("images/images.tsv"); + var imageFolder = Path.GetDirectoryName(dataFile); + var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var images = new ImageLoaderTransform(env, new ImageLoaderTransform.Arguments() + { + Column = new ImageLoaderTransform.Column[1] + { + new ImageLoaderTransform.Column() { Source= "ImagePath", Name="ImageReal" } + }, + ImageFolder = imageFolder + }, data); + + var cropped = new ImageResizerTransform(env, new ImageResizerTransform.Arguments() + { + Column = new ImageResizerTransform.Column[1]{ + new ImageResizerTransform.Column() { Name= "ImageCropped", Source = "ImageReal", ImageHeight =100, ImageWidth = 100, Resizing = ImageResizerTransform.ResizingKind.IsoPad} + } + }, images); + + cropped.Schema.TryGetColumnIndex("ImagePath", out int pathColumn); + cropped.Schema.TryGetColumnIndex("ImageCropped", out int cropBitmapColumn); + using (var cursor = cropped.GetRowCursor((x) => true)) + { + var pathGetter = cursor.GetGetter(pathColumn); + DvText path = default; + var bitmapCropGetter = cursor.GetGetter(cropBitmapColumn); + Bitmap bitmap = default; + while (cursor.MoveNext()) + { + pathGetter(ref path); + bitmapCropGetter(ref bitmap); + Assert.NotNull(bitmap); + var fileToSave = GetOutputPath(Path.GetFileNameWithoutExtension(path.ToString()) + ".cropped.jpg"); + bitmap.Save(fileToSave, System.Drawing.Imaging.ImageFormat.Jpeg); + } + } + } + } + + [Fact] + public void TestGreyscaleTransformImages() + { + using (var env = new TlcEnvironment()) + { + var imageHeight = 150; + var imageWidth = 100; + var dataFile = GetDataPath("images/images.tsv"); + var imageFolder = Path.GetDirectoryName(dataFile); + var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var images = new ImageLoaderTransform(env, new ImageLoaderTransform.Arguments() + { + Column = new ImageLoaderTransform.Column[1] + { + new ImageLoaderTransform.Column() { Source= "ImagePath", Name="ImageReal" } + }, + ImageFolder = imageFolder + }, data); + var cropped = new ImageResizerTransform(env, new ImageResizerTransform.Arguments() + { + Column = new ImageResizerTransform.Column[1]{ + new ImageResizerTransform.Column() { Name= "ImageCropped", Source = "ImageReal", ImageHeight =imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop} + } + }, images); + + var grey = new ImageGrayscaleTransform(env, new ImageGrayscaleTransform.Arguments() + { + Column = new ImageGrayscaleTransform.Column[1]{ + new ImageGrayscaleTransform.Column() { Name= "ImageGrey", Source = "ImageCropped"} + } + }, cropped); + + grey.Schema.TryGetColumnIndex("ImageGrey", out int greyColumn); + using (var cursor = grey.GetRowCursor((x) => true)) + { + var bitmapGetter = cursor.GetGetter(greyColumn); + Bitmap bitmap = default; + while (cursor.MoveNext()) + { + bitmapGetter(ref bitmap); + Assert.NotNull(bitmap); + for (int x = 0; x < imageWidth; x++) + for (int y = 0; y < imageHeight; y++) + { + var pixel = bitmap.GetPixel(x, y); + // greyscale image has same values for R,G and B + Assert.True(pixel.R == pixel.G && pixel.G == pixel.B); + } + } + } + } + } + + [Fact] + public void TestBackAndForthConversion() + { + using (var env = new TlcEnvironment()) + { + var imageHeight = 100; + var imageWidth = 130; + var dataFile = GetDataPath("images/images.tsv"); + var imageFolder = Path.GetDirectoryName(dataFile); + var data = env.CreateLoader("Text{col=ImagePath:TX:0 col=Name:TX:1}", new MultiFileSource(dataFile)); + var images = new ImageLoaderTransform(env, new ImageLoaderTransform.Arguments() + { + Column = new ImageLoaderTransform.Column[1] + { + new ImageLoaderTransform.Column() { Source= "ImagePath", Name="ImageReal" } + }, + ImageFolder = imageFolder + }, data); + var cropped = new ImageResizerTransform(env, new ImageResizerTransform.Arguments() + { + Column = new ImageResizerTransform.Column[1]{ + new ImageResizerTransform.Column() { Source = "ImageReal", Name= "ImageCropped", ImageHeight =imageHeight, ImageWidth = imageWidth, Resizing = ImageResizerTransform.ResizingKind.IsoCrop} + } + }, images); + + var pixels = new ImagePixelExtractorTransform(env, new ImagePixelExtractorTransform.Arguments() + { + Column = new ImagePixelExtractorTransform.Column[1]{ + new ImagePixelExtractorTransform.Column() { Source= "ImageCropped", Name = "ImagePixels", UseAlpha=true} + } + }, cropped); + + var backToBitmaps = new VectorToImageTransform(env, new VectorToImageTransform.Arguments() + { + Column = new VectorToImageTransform.Column[1]{ + new VectorToImageTransform.Column() { Source= "ImagePixels", Name = "ImageRestored" , ImageHeight=imageHeight, ImageWidth=imageWidth, ContainsAlpha=true} + } + }, pixels); + + backToBitmaps.Schema.TryGetColumnIndex("ImageRestored", out int bitmapColumn); + backToBitmaps.Schema.TryGetColumnIndex("ImageCropped", out int cropBitmapColumn); + using (var cursor = backToBitmaps.GetRowCursor((x) => true)) + { + var bitmapGetter = cursor.GetGetter(bitmapColumn); + Bitmap restoredBitmap = default; + + var bitmapCropGetter = cursor.GetGetter(cropBitmapColumn); + Bitmap croppedBitmap = default; + while (cursor.MoveNext()) + { + bitmapGetter(ref restoredBitmap); + Assert.NotNull(restoredBitmap); + bitmapCropGetter(ref croppedBitmap); + Assert.NotNull(croppedBitmap); + for (int x = 0; x < imageWidth; x++) + for (int y = 0; y < imageHeight; y++) + { + Assert.True(croppedBitmap.GetPixel(x, y) == restoredBitmap.GetPixel(x, y)); + } + } + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj index 2a2ea8bca1..ed1b948384 100644 --- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj +++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj @@ -5,6 +5,8 @@ + + @@ -21,5 +23,7 @@ + + \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs b/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs new file mode 100644 index 0000000000..a8b7aaa7ad --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/ApiScenariosTests.cs @@ -0,0 +1,58 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.TestFramework; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + /// + /// Common utility functions for API scenarios tests. + /// + public partial class ApiScenariosTests : BaseTestClass + { + public ApiScenariosTests(ITestOutputHelper output) : base(output) + { + } + + public const string IrisDataPath = "iris.data"; + public const string SentimentDataPath = "wikipedia-detox-250-line-data.tsv"; + public const string SentimentTestPath = "wikipedia-detox-250-line-test.tsv"; + + public class IrisData : IrisDataNoLabel + { + public string Label; + } + + public class IrisDataNoLabel + { + public float SepalLength; + public float SepalWidth; + public float PetalLength; + public float PetalWidth; + } + + public class IrisPrediction + { + public string PredictedLabel; + public float[] Score; + } + + public class SentimentData + { + [ColumnName("Label")] + public bool Sentiment; + public string SentimentText; + } + + public class SentimentPrediction + { + [ColumnName("PredictedLabel")] + public bool Sentiment; + + public float Score; + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs b/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs new file mode 100644 index 0000000000..450736207b --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/AutoNormalizationAndCaching.cs @@ -0,0 +1,49 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Auto-normalization and caching: It should be relatively easy for normalization + /// and caching to be introduced for training, if the trainer supports or would benefit + /// from that. + /// + [Fact] + public void AutoNormalizationAndCaching() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline. + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); + + // Train. + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1, + ConvergenceTolerance = 1f + }); + + // Auto-caching. + IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, trans, prefetch: null) : trans; + var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); + + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + } + + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs new file mode 100644 index 0000000000..84a2d7fe84 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/CrossValidation.cs @@ -0,0 +1,95 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Models; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using System; +using System.Collections.Generic; +using System.Linq; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Cross-validation: Have a mechanism to do cross validation, that is, you come up with + /// a data source (optionally with stratification column), come up with an instantiable transform + /// and trainer pipeline, and it will handle (1) splitting up the data, (2) training the separate + /// pipelines on in-fold data, (3) scoring on the out-fold data, (4) returning the set of + /// evaluations and optionally trained pipes. (People always want metrics out of xfold, + /// they sometimes want the actual models too.) + /// + [Fact] + void CrossValidation() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + int numFolds = 5; + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline. + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var text = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); + IDataView trans = new GenerateNumberTransform(env, text, "StratificationColumn"); + // Train. + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1, + ConvergenceTolerance = 1f + }); + + + var metrics = new List(); + for (int fold = 0; fold < numFolds; fold++) + { + IDataView trainPipe = new RangeFilter(env, new RangeFilter.Arguments() + { + Column = "StratificationColumn", + Min = (Double)fold / numFolds, + Max = (Double)(fold + 1) / numFolds, + Complement = true + }, trans); + trainPipe = new OpaqueDataView(trainPipe); + var trainData = new RoleMappedData(trainPipe, label: "Label", feature: "Features"); + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainData, trainer); + var preCachedData = trainData; + // Auto-caching. + if (trainer.Info.WantCaching) + { + var prefetch = trainData.Schema.GetColumnRoles().Select(kc => kc.Value.Index).ToArray(); + var cacheView = new CacheDataView(env, trainData.Data, prefetch); + // Because the prefetching worked, we know that these are valid columns. + trainData = new RoleMappedData(cacheView, trainData.Schema.GetColumnRoleNames()); + } + + var predictor = trainer.Train(new Runtime.TrainContext(trainData)); + IDataView testPipe = new RangeFilter(env, new RangeFilter.Arguments() + { + Column = "StratificationColumn", + Min = (Double)fold / numFolds, + Max = (Double)(fold + 1) / numFolds, + Complement = false + }, trans); + testPipe = new OpaqueDataView(testPipe); + var pipe = ApplyTransformUtils.ApplyAllTransformsToData(env, preCachedData.Data, testPipe, trainPipe); + + var testRoles = new RoleMappedData(pipe, trainData.Schema.GetColumnRoleNames()); + + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, testRoles, env, testRoles.Schema); + + BinaryClassifierMamlEvaluator eval = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments() { }); + var dataEval = new RoleMappedData(scorer, testRoles.Schema.GetColumnRoleNames(), opt: true); + var dict = eval.Evaluate(dataEval); + var foldMetrics = BinaryClassificationMetrics.FromMetrics(env, dict["OverallMetrics"], dict["ConfusionMatrix"]); + metrics.Add(foldMetrics.Single()); + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs new file mode 100644 index 0000000000..fc20be754e --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/DecomposableTrainAndPredict.cs @@ -0,0 +1,60 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using System.Linq; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + + public partial class ApiScenariosTests + { + /// + /// Decomposable train and predict: Train on Iris multiclass problem, which will require + /// a transform on labels. Be able to reconstitute the pipeline for a prediction only task, + /// which will essentially "drop" the transform over labels, while retaining the property + /// that the predicted label for this has a key-type, the probability outputs for the classes + /// have the class labels as slot names, etc. This should be do-able without ugly compromises like, + /// say, injecting a dummy label. + /// + [Fact] + void DecomposableTrainAndPredict() + { + var dataPath = GetDataPath(IrisDataPath); + using (var env = new TlcEnvironment()) + { + var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var term = new TermTransform(env, loader, "Label"); + var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"); + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }); + + IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; + var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); + + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + // Cut out term transform from pipeline. + var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term); + var keyToValue = new KeyToValueTransform(env, newScorer, "PredictedLabel"); + var model = env.CreatePredictionEngine(keyToValue); + + var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var testData = testLoader.AsEnumerable(env, false); + foreach (var input in testData.Take(20)) + { + var prediction = model.Predict(input); + Assert.True(prediction.PredictedLabel == "Iris-setosa"); + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs new file mode 100644 index 0000000000..a67b9d56a9 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Evaluation.cs @@ -0,0 +1,62 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Xunit; +using Microsoft.ML.Models; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Evaluation: Similar to the simple train scenario, except instead of having some + /// predictive structure, be able to score another "test" data file, run the result + /// through an evaluator and get metrics like AUC, accuracy, PR curves, and whatnot. + /// Getting metrics out of this shoudl be as straightforward and unannoying as possible. + /// + [Fact] + public void Evaluation() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + + // Train + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + + var cached = new CacheDataView(env, trans, prefetch: null); + var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + // Create prediction engine and test predictions. + var model = env.CreatePredictionEngine(scorer); + + // Take a couple examples out of the test data and run predictions on top. + var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testData = testLoader.AsEnumerable(env, false); + + var dataEval = new RoleMappedData(scorer, label: "Label", feature: "Features", opt: true); + + var evaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments() { }); + var metricsDict = evaluator.Evaluate(dataEval); + + var metrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0]; + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs new file mode 100644 index 0000000000..017d2675db --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Extensibility.cs @@ -0,0 +1,62 @@ +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using System; +using System.Linq; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Extensibility: We can't possibly write every conceivable transform and should not try. + /// It should somehow be possible for a user to inject custom code to, say, transform data. + /// This might have a much steeper learning curve than the other usages (which merely involve + /// usage of already established components), but should still be possible. + /// + [Fact] + void Extensibility() + { + var dataPath = GetDataPath(IrisDataPath); + using (var env = new TlcEnvironment()) + { + var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + Action action = (i, j) => + { + j.Label = i.Label; + j.PetalLength = i.SepalLength > 3 ? i.PetalLength : i.SepalLength; + j.PetalWidth = i.PetalWidth; + j.SepalLength = i.SepalLength; + j.SepalWidth = i.SepalWidth; + }; + var lambda = LambdaTransform.CreateMap(env, loader, action); + var term = new TermTransform(env, lambda, "Label"); + var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"); + + var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments { MaxIterations = 100, Shuffle = true, NumThreads = 1 }); + + IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; + var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); + + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel"); + var model = env.CreatePredictionEngine(keyToValue); + + var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var testData = testLoader.AsEnumerable(env, false); + foreach (var input in testData.Take(20)) + { + var prediction = model.Predict(input); + Assert.True(prediction.PredictedLabel == input.Label); + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs b/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs new file mode 100644 index 0000000000..7b534c1084 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/FileBasedSavingOfData.cs @@ -0,0 +1,52 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Learners; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// File-based saving of data: Come up with transform pipeline. Transform training and + /// test data, and save the featurized data to some file, using the .idv format. + /// Train and evaluate multiple models over that pre-featurized data. (Useful for + /// sweeping scenarios, where you are training many times on the same data, + /// and don't necessarily want to transform it every single time.) + /// + [Fact] + void FileBasedSavingOfData() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + var saver = new BinarySaver(env, new BinarySaver.Arguments()); + using (var ch = env.Start("SaveData")) + using (var file = env.CreateOutputFile("i.idv")) + { + DataSaverUtils.SaveDataView(ch, saver, trans, file); + } + + var binData = new BinaryLoader(env, new BinaryLoader.Arguments(), new MultiFileSource("i.idv")); + var trainRoles = new RoleMappedData(binData, label: "Label", feature: "Features"); + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + DeleteOutputPath("i.idv"); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs b/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs new file mode 100644 index 0000000000..00e4f9b703 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/IntrospectiveTraining.cs @@ -0,0 +1,118 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Calibration; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.TextAnalytics; +using System.Collections.Generic; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + + public partial class ApiScenariosTests + { + private TOut GetValue(Dictionary keyValues, string key) + { + if (keyValues.ContainsKey(key)) + return (TOut)keyValues[key]; + + return default(TOut); + } + + /// + /// Introspective training: Models that produce outputs and are otherwise black boxes are of limited use; + /// it is also necessary often to understand at least to some degree what was learnt. To outline critical + /// scenarios that have come up multiple times: + /// *) When I train a linear model, I should be able to inspect coefficients. + /// *) The tree ensemble learners, I should be able to inspect the trees. + /// *) The LDA transform, I should be able to inspect the topics. + /// I view it as essential from a usability perspective that this be discoverable to someone without + /// having to read documentation.E.g.: if I have var lda = new LdaTransform().Fit(data)(I don't insist on that + /// exact signature, just giving the idea), then if I were to type lda. + /// In Visual Studio, one of the auto-complete targets should be something like GetTopics. + /// + + [Fact] + void IntrospectiveTraining() + { + var dataPath = GetDataPath(SentimentDataPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var words = WordBagTransform.Create(env, new WordBagTransform.Arguments() + { + NgramLength = 1, + Column = new[] { new WordBagTransform.Column() { Name = "Tokenize", Source = new[] { "SentimentText" } } } + }, loader); + + var lda = new LdaTransform(env, new LdaTransform.Arguments() + { + NumTopic = 10, + NumIterations = 3, + NumThreads = 1, + Column = new[] { new LdaTransform.Column { Source = "Tokenize", Name = "Features"} + } + }, words); + var trainData = lda; + + var cachedTrain = new CacheDataView(env, trainData, prefetch: null); + // Train the first predictor. + var linearTrainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + var trainRoles = new RoleMappedData(cachedTrain, label: "Label", feature: "Features"); + var linearPredictor = linearTrainer.Train(new Runtime.TrainContext(trainRoles)); + VBuffer weights = default; + linearPredictor.GetFeatureWeights(ref weights); + + var topicSummary = lda.GetTopicSummary(); + var treeTrainer = new FastTreeBinaryClassificationTrainer(env, + new FastTreeBinaryClassificationTrainer.Arguments + { + NumTrees = 2 + } + ); + var ftPredictor = treeTrainer.Train(new Runtime.TrainContext(trainRoles)); + FastTreeBinaryPredictor treePredictor; + if (ftPredictor is CalibratedPredictorBase calibrator) + treePredictor = (FastTreeBinaryPredictor)calibrator.SubPredictor; + else + treePredictor = (FastTreeBinaryPredictor)ftPredictor; + var featureNameCollection = FeatureNameCollection.Create(trainRoles.Schema); + foreach (var tree in treePredictor.GetTrees()) + { + var lteChild = tree.LteChild; + var gteChild = tree.GtChild; + // Get nodes. + for (var i = 0; i < tree.NumNodes; i++) + { + var node = tree.GetNode(i, false, featureNameCollection); + var gainValue = GetValue(node.KeyValues, "GainValue"); + var splitGain = GetValue(node.KeyValues, "SplitGain"); + var featureName = GetValue(node.KeyValues, "SplitName"); + var previousLeafValue = GetValue(node.KeyValues, "PreviousLeafValue"); + var threshold = GetValue(node.KeyValues, "Threshold").Split(new[] { ' ' }, 2)[1]; + var nodeIndex = i; + } + // Get leaves. + for (var i = 0; i < tree.NumLeaves; i++) + { + var node = tree.GetNode(i, true, featureNameCollection); + var leafValue = GetValue(node.KeyValues, "LeafValue"); + var extras = GetValue(node.KeyValues, "Extras"); + var nodeIndex = ~i; + } + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs new file mode 100644 index 0000000000..811f857046 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Metacomponents.cs @@ -0,0 +1,57 @@ +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Learners; +using System.Linq; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Meta-components: Meta-components (e.g., components that themselves instantiate components) should not be booby-trapped. + /// When specifying what trainer OVA should use, a user will be able to specify any binary classifier. + /// If they specify a regression or multi-class classifier ideally that should be a compile error. + /// + [Fact] + void Metacomponents() + { + var dataPath = GetDataPath(IrisDataPath); + using (var env = new TlcEnvironment()) + { + var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var term = new TermTransform(env, loader, "Label"); + var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"); + var trainer = new Ova(env, new Ova.Arguments + { + PredictorType = ComponentFactoryUtils.CreateFromFunction( + (e) => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments())) + }); + + IDataView trainData = trainer.Info.WantCaching ? (IDataView)new CacheDataView(env, concat, prefetch: null) : concat; + var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features"); + + // Auto-normalization. + NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel"); + var model = env.CreatePredictionEngine(keyToValue); + + var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath)); + var testData = testLoader.AsEnumerable(env, false); + foreach (var input in testData.Take(20)) + { + var prediction = model.Predict(input); + Assert.True(prediction.PredictedLabel == input.Label); + } + } + } + } +} \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs new file mode 100644 index 0000000000..23fab1c1d6 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/MultithreadedPrediction.cs @@ -0,0 +1,64 @@ +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using System.Linq; +using System.Threading.Tasks; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Multi-threaded prediction. A twist on "Simple train and predict", where we account that + /// multiple threads may want predictions at the same time. Because we deliberately do not + /// reallocate internal memory buffers on every single prediction, the PredictionEngine + /// (or its estimator/transformer based successor) is, like most stateful .NET objects, + /// fundamentally not thread safe. This is deliberate and as designed. However, some mechanism + /// to enable multi-threaded scenarios (e.g., a web server servicing requests) should be possible + /// and performant in the new API. + /// + [Fact] + void MultithreadedPrediction() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + + // Train + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + + var cached = new CacheDataView(env, trans, prefetch: null); + var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + // Create prediction engine and test predictions. + var model = env.CreatePredictionEngine(scorer); + + // Take a couple examples out of the test data and run predictions on top. + var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testData = testLoader.AsEnumerable(env, false); + + Parallel.ForEach(testData, (input) => + { + lock (model) + { + var prediction = model.Predict(input); + } + }); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs b/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs new file mode 100644 index 0000000000..5f87b60958 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/ReconfigurablePrediction.cs @@ -0,0 +1,64 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Models; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Reconfigurable predictions: The following should be possible: A user trains a binary classifier, + /// and through the test evaluator gets a PR curve, the based on the PR curve picks a new threshold + /// and configures the scorer (or more precisely instantiates a new scorer over the same predictor) + /// with some threshold derived from that. + /// + [Fact] + void ReconfigurablePrediction() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + + // Train + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + + var cached = new CacheDataView(env, trans, prefetch: null); + var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + var dataEval = new RoleMappedData(scorer, label: "Label", feature: "Features", opt: true); + + var evaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments() { }); + var metricsDict = evaluator.Evaluate(dataEval); + + var metrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0]; + + var bindable = ScoreUtils.GetSchemaBindableMapper(env, predictor, null); + var mapper = bindable.Bind(env, trainRoles.Schema); + var newScorer = new BinaryClassifierScorer(env, new BinaryClassifierScorer.Arguments { Threshold = 0.01f, ThresholdColumn = DefaultColumnNames.Probability }, + scoreRoles.Data, mapper, trainRoles.Schema); + + dataEval = new RoleMappedData(newScorer, label: "Label", feature: "Features", opt: true); + var newEvaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments() { Threshold = 0.01f, UseRawScoreThreshold = false }); + metricsDict = newEvaluator.Evaluate(dataEval); + var newMetrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0]; + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs new file mode 100644 index 0000000000..246281777c --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/SimpleTrainAndPredict.cs @@ -0,0 +1,150 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Xunit; +using System.Linq; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Start with a dataset in a text file. Run text featurization on text values. + /// Train a linear model over that. (I am thinking sentiment classification.) + /// Out of the result, produce some structure over which you can get predictions programmatically + /// (e.g., the prediction does not happen over a file as it did during training). + /// + [Fact] + public void SimpleTrainAndPredict() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + + // Train + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + + var cached = new CacheDataView(env, trans, prefetch: null); + var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema); + + // Create prediction engine and test predictions. + var model = env.CreatePredictionEngine(scorer); + + // Take a couple examples out of the test data and run predictions on top. + var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testData = testLoader.AsEnumerable(env, false); + foreach (var input in testData.Take(5)) + { + var prediction = model.Predict(input); + // Verify that predictions match and scores are separated from zero. + Assert.Equal(input.Sentiment, prediction.Sentiment); + Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1); + } + } + } + + private static TextTransform.Arguments MakeSentimentTextTransformArgs(bool normalize = true) + { + return new TextTransform.Arguments() + { + Column = new TextTransform.Column + { + Name = "Features", + Source = new[] { "SentimentText" } + }, + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode.Lower, + OutputTokens = true, + StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(), + VectorNormalizer = normalize ? TextTransform.TextNormKind.L2 : TextTransform.TextNormKind.None, + CharFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments() { NgramLength = 3, AllLengths = false }, + WordFeatureExtractor = new NgramExtractorTransform.NgramExtractorArguments() { NgramLength = 2, AllLengths = true }, + }; + } + + private static TextLoader.Arguments MakeIrisTextLoaderArgs() + { + + return new TextLoader.Arguments() + { + Separator = "comma", + HasHeader = true, + Column = new[] + { + new TextLoader.Column() + { + Name = "SepalLength", + Source = new [] { new TextLoader.Range() { Min=0, Max=0} }, + Type = DataKind.R4 + }, + new TextLoader.Column() + { + Name = "SepalWidth", + Source = new [] { new TextLoader.Range() { Min=1, Max=1} }, + Type = DataKind.R4 + }, + new TextLoader.Column() + { + Name = "PetalLength", + Source = new [] { new TextLoader.Range() { Min=2, Max=2} }, + Type = DataKind.R4 + }, + new TextLoader.Column() + { + Name = "PetalWidth", + Source = new [] { new TextLoader.Range() { Min=3, Max=3} }, + Type = DataKind.R4 + }, + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min=4, Max=4} }, + Type = DataKind.Text + } + } + }; + } + private static TextLoader.Arguments MakeSentimentTextLoaderArgs() + { + return new TextLoader.Arguments() + { + Separator = "tab", + HasHeader = true, + Column = new[] + { + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min=0, Max=0} }, + Type = DataKind.BL + }, + + new TextLoader.Column() + { + Name = "SentimentText", + Source = new [] { new TextLoader.Range() { Min=1, Max=1} }, + Type = DataKind.Text + } + } + }; + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs new file mode 100644 index 0000000000..51ae621c1b --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainSaveModelAndPredict.cs @@ -0,0 +1,70 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using System.Linq; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Train, save/load model, predict: + /// Serve the scenario where training and prediction happen in different processes (or even different machines). + /// The actual test will not run in different processes, but will simulate the idea that the + /// "communication pipe" is just a serialized model of some form. + /// + [Fact] + public void TrainSaveModelAndPredict() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + + // Train + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + + var cached = new CacheDataView(env, trans, prefetch: null); + var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + PredictionEngine model; + using (var file = env.CreateTempFile()) + { + // Save model. + var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + using (var ch = env.Start("saving")) + TrainUtils.SaveModel(env, ch, file, predictor, scoreRoles); + + // Load model. + using (var fs = file.OpenReadStream()) + model = env.CreatePredictionEngine(fs); + } + + // Take a couple examples out of the test data and run predictions on top. + var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath))); + var testData = testLoader.AsEnumerable(env, false); + foreach (var input in testData.Take(5)) + { + var prediction = model.Predict(input); + // Verify that predictions match and scores are separated from zero. + Assert.Equal(input.Sentiment, prediction.Sentiment); + Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1); + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs new file mode 100644 index 0000000000..a54469b348 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithInitialPredictor.cs @@ -0,0 +1,46 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Learners; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Train with initial predictor: Similar to the simple train scenario, but also accept a pre-trained initial model. + /// The scenario might be one of the online linear learners that can take advantage of this, e.g., averaged perceptron. + /// + [Fact] + public void TrainWithInitialPredictor() + { + var dataPath = GetDataPath(SentimentDataPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + var trainData = trans; + + var cachedTrain = new CacheDataView(env, trainData, prefetch: null); + // Train the first predictor. + var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments + { + NumThreads = 1 + }); + var trainRoles = new RoleMappedData(cachedTrain, label: "Label", feature: "Features"); + var predictor = trainer.Train(new Runtime.TrainContext(trainRoles)); + + // Train the second predictor on the same data. + var secondTrainer = new AveragedPerceptronTrainer(env, new AveragedPerceptronTrainer.Arguments()); + var finalPredictor = secondTrainer.Train(new TrainContext(trainRoles, initialPredictor: predictor)); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs new file mode 100644 index 0000000000..1166a1bfa8 --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/TrainWithValidationSet.cs @@ -0,0 +1,54 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FastTree; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Train with validation set: Similar to the simple train scenario, but also support a validation set. + /// The learner might be trees with early stopping. + /// + [Fact] + public void TrainWithValidationSet() + { + var dataPath = GetDataPath(SentimentDataPath); + var validationDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader); + var trainData = trans; + + // Apply the same transformations on the validation set. + // Sadly, there is no way to easily apply the same loader to different data, so we either have + // to create another loader, or to save the loader to model file and then reload. + + // A new one is not always feasible, but this time it is. + var validLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(validationDataPath)); + var validData = ApplyTransformUtils.ApplyAllTransformsToData(env, trainData, validLoader); + + // Cache both datasets. + var cachedTrain = new CacheDataView(env, trainData, prefetch: null); + var cachedValid = new CacheDataView(env, validData, prefetch: null); + + // Train. + var trainer = new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments + { + NumTrees = 3 + }); + var trainRoles = new RoleMappedData(cachedTrain, label: "Label", feature: "Features"); + var validRoles = new RoleMappedData(cachedValid, label: "Label", feature: "Features"); + trainer.Train(new Runtime.TrainContext(trainRoles, validRoles)); + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs new file mode 100644 index 0000000000..97bb01158e --- /dev/null +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Visibility.cs @@ -0,0 +1,59 @@ +using Microsoft.ML.Runtime.Data; +using Xunit; + +namespace Microsoft.ML.Tests.Scenarios.Api +{ + public partial class ApiScenariosTests + { + /// + /// Visibility: It should, possibly through the debugger, be not such a pain to actually + /// see what is happening to your data when you apply this or that transform. E.g.: if I + /// were to have the text "Help I'm a bug!" I should be able to see the steps where it is + /// normalized to "help i'm a bug" then tokenized into ["help", "i'm", "a", "bug"] then + /// mapped into term numbers [203, 25, 3, 511] then projected into the sparse + /// float vector {3:1, 25:1, 203:1, 511:1}, etc. etc. + /// + [Fact] + void Visibility() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline. + var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath)); + + var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(false), loader); + + // In order to find out available column names, you can go through schema and check + // column names and appropriate type for getter. + for( int i=0; i< trans.Schema.ColumnCount; i++) + { + var columnName = trans.Schema.GetColumnName(i); + var columnType = trans.Schema.GetColumnType(i).RawType; + } + + using (var cursor = trans.GetRowCursor(x => true)) + { + Assert.True(cursor.Schema.TryGetColumnIndex("SentimentText", out int textColumn)); + Assert.True(cursor.Schema.TryGetColumnIndex("Features_TransformedText", out int transformedTextColumn)); + Assert.True(cursor.Schema.TryGetColumnIndex("Features", out int featureColumn)); + + var originalTextGettter = cursor.GetGetter(textColumn); + var transformedTextGettter = cursor.GetGetter>(transformedTextColumn); + var featureGettter = cursor.GetGetter>(featureColumn); + DvText text = default; + VBuffer transformedText = default; + VBuffer features = default; + while (cursor.MoveNext()) + { + originalTextGettter(ref text); + transformedTextGettter(ref transformedText); + featureGettter(ref features); + } + } + } + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs index 9c4283df45..4d522c6f4d 100644 --- a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs @@ -32,6 +32,18 @@ public void TrainAndPredictSentimentModelTest() ValidateBinaryMetrics(metrics); } + [Fact] + public void TrainAndPredictSymSGDSentimentModelTest() + { + var pipeline = PreparePipelineSymSGD(); + var model = pipeline.Train(); + var testData = PrepareTextLoaderTestData(); + var evaluator = new BinaryClassificationEvaluator(); + var metrics = evaluator.Evaluate(model, testData); + ValidateExamplesSymSGD(model); + ValidateBinaryMetricsSymSGD(metrics); + } + [Fact] public void TrainAndPredictLightGBMSentimentModelTest() { @@ -175,6 +187,39 @@ public void CrossValidateSentimentModelTest() Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); } + private void ValidateBinaryMetricsSymSGD(BinaryClassificationMetrics metrics) + { + + Assert.Equal(.8889, metrics.Accuracy, 4); + Assert.Equal(1, metrics.Auc, 1); + Assert.Equal(0.96, metrics.Auprc, 2); + Assert.Equal(1, metrics.Entropy, 3); + Assert.Equal(.9, metrics.F1Score, 4); + Assert.Equal(.97, metrics.LogLoss, 3); + Assert.Equal(3.030, metrics.LogLossReduction, 3); + Assert.Equal(1, metrics.NegativePrecision, 3); + Assert.Equal(.778, metrics.NegativeRecall, 3); + Assert.Equal(.818, metrics.PositivePrecision, 3); + Assert.Equal(1, metrics.PositiveRecall); + + var matrix = metrics.ConfusionMatrix; + Assert.Equal(2, matrix.Order); + Assert.Equal(2, matrix.ClassNames.Count); + Assert.Equal("positive", matrix.ClassNames[0]); + Assert.Equal("negative", matrix.ClassNames[1]); + + Assert.Equal(9, matrix[0, 0]); + Assert.Equal(9, matrix["positive", "positive"]); + Assert.Equal(0, matrix[0, 1]); + Assert.Equal(0, matrix["positive", "negative"]); + + Assert.Equal(2, matrix[1, 0]); + Assert.Equal(2, matrix["negative", "positive"]); + Assert.Equal(7, matrix[1, 1]); + Assert.Equal(7, matrix["negative", "negative"]); + + } + private void ValidateBinaryMetricsLightGBM(BinaryClassificationMetrics metrics) { @@ -338,6 +383,55 @@ private LearningPipeline PreparePipelineLightGBM() return pipeline; } + private LearningPipeline PreparePipelineSymSGD() + { + var dataPath = GetDataPath(SentimentDataPath); + var pipeline = new LearningPipeline(); + + pipeline.Add(new Data.TextLoader(dataPath) + { + Arguments = new TextLoaderArguments + { + Separator = new[] { '\t' }, + HasHeader = true, + Column = new[] + { + new TextLoaderColumn() + { + Name = "Label", + Source = new [] { new TextLoaderRange(0) }, + Type = Data.DataKind.Num + }, + + new TextLoaderColumn() + { + Name = "SentimentText", + Source = new [] { new TextLoaderRange(1) }, + Type = Data.DataKind.Text + } + } + } + }); + + pipeline.Add(new TextFeaturizer("Features", "SentimentText") + { + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = TextNormalizerTransformCaseNormalizationMode.Lower, + OutputTokens = true, + StopWordsRemover = new PredefinedStopWordsRemover(), + VectorNormalizer = TextTransformTextNormKind.L2, + CharFeatureExtractor = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false }, + WordFeatureExtractor = new NGramNgramExtractor() { NgramLength = 2, AllLengths = true } + }); + + + pipeline.Add(new SymSgdBinaryClassifier() { NumberOfThreads = 1}); + + pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" }); + return pipeline; + } + private void ValidateExamples(PredictionModel model, bool useLightGBM = false) { var sentiments = GetTestData(); @@ -359,6 +453,16 @@ private void ValidateExamplesLightGBM(PredictionModel model) + { + var sentiments = GetTestData(); + var predictions = model.Predict(sentiments); + Assert.Equal(2, predictions.Count()); + + Assert.True(predictions.ElementAt(0).Sentiment.IsFalse); + Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + } + private Data.TextLoader PrepareTextLoaderTestData() { var testDataPath = GetDataPath(SentimentTestPath); diff --git a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/SentimentPredictionTests.cs index bea81175d8..7dd7a0145c 100644 --- a/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/SentimentPredictionTests.cs +++ b/test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/SentimentPredictionTests.cs @@ -100,6 +100,99 @@ public void TrainAndPredictSentimentModelWithDirectionInstantiationTest() } } + [Fact] + public void TrainAndPredictSentimentModelWithDirectionInstantiationTestWithWordEmbedding() + { + var dataPath = GetDataPath(SentimentDataPath); + var testDataPath = GetDataPath(SentimentTestPath); + + using (var env = new TlcEnvironment(seed: 1, conc: 1)) + { + // Pipeline + var loader = new TextLoader(env, + new TextLoader.Arguments() + { + Separator = "tab", + HasHeader = true, + Column = new[] + { + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min=0, Max=0} }, + Type = DataKind.Num + }, + + new TextLoader.Column() + { + Name = "SentimentText", + Source = new [] { new TextLoader.Range() { Min=1, Max=1} }, + Type = DataKind.Text + } + } + }, new MultiFileSource(dataPath)); + + var text = TextTransform.Create(env, new TextTransform.Arguments() + { + Column = new TextTransform.Column + { + Name = "WordEmbeddings", + Source = new[] { "SentimentText" } + }, + KeepDiacritics = false, + KeepPunctuations = false, + TextCase = Runtime.TextAnalytics.TextNormalizerTransform.CaseNormalizationMode.Lower, + OutputTokens = true, + StopWordsRemover = new Runtime.TextAnalytics.PredefinedStopWordsRemoverFactory(), + VectorNormalizer = TextTransform.TextNormKind.None, + CharFeatureExtractor = null, + WordFeatureExtractor = null, + }, + loader); + + var trans = new WordEmbeddingsTransform(env, new WordEmbeddingsTransform.Arguments() + { + Column = new WordEmbeddingsTransform.Column[1] + { + new WordEmbeddingsTransform.Column + { + Name = "Features", + Source = "WordEmbeddings_TransformedText" + } + }, + ModelKind = WordEmbeddingsTransform.PretrainedModelKind.Sswe, + }, text); + // Train + var trainer = new FastTreeBinaryClassificationTrainer(env, new FastTreeBinaryClassificationTrainer.Arguments() + { + NumLeaves = 5, + NumTrees = 5, + MinDocumentsInLeafs = 2 + }); + + var trainRoles = new RoleMappedData(trans, label: "Label", feature: "Features"); + var pred = trainer.Train(trainRoles); + // Get scorer and evaluate the predictions from test data + IDataScorerTransform testDataScorer = GetScorer(env, trans, pred, testDataPath); + var metrics = EvaluateBinary(env, testDataScorer); + + // SSWE is a simple word embedding model + we train on a really small dataset, so metrics are not great. + Assert.Equal(.6667, metrics.Accuracy, 4); + Assert.Equal(.71, metrics.Auc, 1); + Assert.Equal(.58, metrics.Auprc, 2); + // Create prediction engine and test predictions + var model = env.CreateBatchPredictionEngine(testDataScorer); + var sentiments = GetTestData(); + var predictions = model.Predict(sentiments, false); + Assert.Equal(2, predictions.Count()); + Assert.True(predictions.ElementAt(0).Sentiment.IsTrue); + Assert.True(predictions.ElementAt(1).Sentiment.IsTrue); + + // Get feature importance based on feature gain during training + var summary = ((FeatureWeightsCalibratedPredictor)pred).GetSummaryInKeyValuePairs(trainRoles.Schema); + Assert.Equal(1.0, (double)summary[0].Value, 1); + } + } private BinaryClassificationMetrics EvaluateBinary(IHostEnvironment env, IDataView scoredData) { var dataEval = new RoleMappedData(scoredData, label: "Label", feature: "Features", opt: true); diff --git a/test/Microsoft.ML.Tests/TextLoaderTests.cs b/test/Microsoft.ML.Tests/TextLoaderTests.cs index 61a1744dfb..50a7e55975 100644 --- a/test/Microsoft.ML.Tests/TextLoaderTests.cs +++ b/test/Microsoft.ML.Tests/TextLoaderTests.cs @@ -228,7 +228,7 @@ public void CanSuccessfullyTrimSpaces() public void ThrowsExceptionWithPropertyName() { Exception ex = Assert.Throws( () => new Data.TextLoader("fakefile.txt").CreateFrom() ); - Assert.StartsWith("String1 is missing ColumnAttribute", ex.Message); + Assert.StartsWith("Field or property String1 is missing ColumnAttribute", ex.Message); } public class QuoteInput diff --git a/test/data/images/banana.jpg b/test/data/images/banana.jpg new file mode 100644 index 0000000000..45cb14b607 Binary files /dev/null and b/test/data/images/banana.jpg differ diff --git a/test/data/images/hotdog.jpg b/test/data/images/hotdog.jpg new file mode 100644 index 0000000000..94c918c7dc Binary files /dev/null and b/test/data/images/hotdog.jpg differ diff --git a/test/data/images/images.tsv b/test/data/images/images.tsv new file mode 100644 index 0000000000..0315f7865b --- /dev/null +++ b/test/data/images/images.tsv @@ -0,0 +1,3 @@ +banana.jpg banana +hotdog.jpg hotdog +tomato.jpg tomato \ No newline at end of file diff --git a/test/data/images/tomato.jpg b/test/data/images/tomato.jpg new file mode 100644 index 0000000000..b101856e14 Binary files /dev/null and b/test/data/images/tomato.jpg differ diff --git a/test/run-tests.proj b/test/run-tests.proj index dd2433b3c5..a5afe75dd3 100644 --- a/test/run-tests.proj +++ b/test/run-tests.proj @@ -3,6 +3,7 @@ +