From 9769cad0d8899086368bd4ecde9b164f39008472 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Fri, 8 Sep 2023 12:53:10 -0600 Subject: [PATCH 01/17] added in win-arm64 --- .vsts-dotnet-ci.yml | 27 +++++++++++++++-- build/vsts-ci.yml | 30 +++++++++++++++++-- eng/pkg/CommonPackage.props | 8 +++++ eng/pkg/Pack.props | 9 ++++++ .../Microsoft.ML.AutoML.csproj | 1 + .../netstandard2.0/Microsoft.ML.CpuMath.props | 7 +++++ .../Microsoft.ML.Recommender.csproj | 1 + src/Microsoft.ML/Microsoft.ML.csproj | 1 + 8 files changed, 79 insertions(+), 5 deletions(-) diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index 846f15f004..8b514276c5 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -131,7 +131,30 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x64_Net60 + architecture: arm64 + name: Windows_cross_arm64_Net6 + buildScript: build.cmd + customMatrixes: + Debug_Build: + _configuration: Debug + _config_short: DI + _includeBenchmarkData: false + _targetFramework: net6.0 + Release_Build: + _configuration: Release + _config_short: RI + _includeBenchmarkData: false + _targetFramework: net6.0 + innerLoop: true + vsTestConfiguration: "/Framework:.NETCoreApp,Version=v6.0" + pool: + name: NetCore-Public + demands: ImageOverride -equals 1es-windows-2019-open + helixQueue: Windows.10.Arm64.Open + +- template: /build/ci/job-template.yml + parameters: + name: Windows_x64_Net6 buildScript: build.cmd innerLoop: true vsTestConfiguration: "/Framework:.NETCoreApp,Version=v3.1" @@ -164,7 +187,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x86_Net60 + name: Windows_x86_Net6 architecture: x86 buildScript: build.cmd innerLoop: true diff --git a/build/vsts-ci.yml b/build/vsts-ci.yml index b3eb3e61d2..9487484d14 100644 --- a/build/vsts-ci.yml +++ b/build/vsts-ci.yml @@ -63,7 +63,7 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish Linux package assets + displayName: Publish Linux_arm package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -86,7 +86,7 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish Linux package assets + displayName: Publish Linux_arm64 package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -133,7 +133,27 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish macOS package assets + displayName: Publish macOS_M1 package assets + inputs: + pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets + artifactName: pkgassets + artifactType: container + + - template: /eng/common/templates/steps/generate-sbom.yml + + ################################################################################ + - job: Windows_arm64 + ################################################################################ + pool: + name: NetCore1ESPool-Internal + demands: ImageOverride -equals windows.vs2019.amd64 + steps: + # Only build native assets to avoid conflicts. + - script: ./build.cmd -projects $(Build.SourcesDirectory)/src/Native/Native.proj -configuration $(BuildConfig) /p:TargetArchitecture=arm64 /p:CopyPackageAssets=true + displayName: Build Native Assets + + - task: PublishBuildArtifacts@1 + displayName: Publish Windows_arm64 package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -141,6 +161,10 @@ stages: - template: /eng/common/templates/steps/generate-sbom.yml + # Terminate all dotnet build processes. + - script: $(Build.SourcesDirectory)/.dotnet/dotnet.exe build-server shutdown + displayName: Dotnet Server Shutdown + ################################################################################ - job: Windows_x86 ################################################################################ diff --git a/eng/pkg/CommonPackage.props b/eng/pkg/CommonPackage.props index 08fbb153af..dddf5970c6 100644 --- a/eng/pkg/CommonPackage.props +++ b/eng/pkg/CommonPackage.props @@ -13,12 +13,20 @@ false %(Filename)%(Extension) + PreserveNewest false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + \ No newline at end of file diff --git a/eng/pkg/Pack.props b/eng/pkg/Pack.props index e436b01c1a..888000e570 100644 --- a/eng/pkg/Pack.props +++ b/eng/pkg/Pack.props @@ -58,12 +58,21 @@ false %(Filename)%(Extension) + PreserveNewest false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + + diff --git a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj index 6ecc501c11..669afe59de 100644 --- a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj +++ b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj @@ -86,6 +86,7 @@ + diff --git a/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props b/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props index ce379e2f00..bdc552e295 100644 --- a/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props +++ b/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props @@ -19,6 +19,13 @@ false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + \ No newline at end of file diff --git a/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj b/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj index 92955776e8..980e5719da 100644 --- a/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj +++ b/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj @@ -26,6 +26,7 @@ + \ No newline at end of file diff --git a/src/Microsoft.ML/Microsoft.ML.csproj b/src/Microsoft.ML/Microsoft.ML.csproj index 780c6048a1..9b8fdf7538 100644 --- a/src/Microsoft.ML/Microsoft.ML.csproj +++ b/src/Microsoft.ML/Microsoft.ML.csproj @@ -66,6 +66,7 @@ + From f167be58194699d05a720192be9944eb11037990 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Mon, 11 Sep 2023 16:21:00 -0600 Subject: [PATCH 02/17] fixed MKL arm64 cmake issue --- src/Native/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/Native/CMakeLists.txt b/src/Native/CMakeLists.txt index 6624e63503..605fe6f700 100644 --- a/src/Native/CMakeLists.txt +++ b/src/Native/CMakeLists.txt @@ -268,6 +268,9 @@ else() if (DEFINED ONEDAL_DEVEL_PATH) message("Path to OneDal library defined [${ONEDAL_DEVEL_PATH}] but it is not being built on this arch.") endif() + if (DEFINED MKL_LIB_PATH) + message("Path to Intel MKL library defined [${MKL_LIB_PATH}] but it is not being built on this arch.") + endif() endif() add_subdirectory(LdaNative) From 170d46790bce2aabfccc5ef3c8585bc821d1789b Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Wed, 13 Sep 2023 11:16:02 -0600 Subject: [PATCH 03/17] right helix queue --- .vsts-dotnet-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index 8b514276c5..65cb1eb45b 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -150,7 +150,7 @@ jobs: pool: name: NetCore-Public demands: ImageOverride -equals 1es-windows-2019-open - helixQueue: Windows.10.Arm64.Open + helixQueue: windows.11.arm64.open - template: /build/ci/job-template.yml parameters: From f246cfc4951ebeceed482151269f6a95342db4de Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Fri, 8 Sep 2023 12:53:10 -0600 Subject: [PATCH 04/17] added in win-arm64 --- .vsts-dotnet-ci.yml | 27 +++++++++++++++-- build/vsts-ci.yml | 30 +++++++++++++++++-- eng/pkg/CommonPackage.props | 8 +++++ eng/pkg/Pack.props | 9 ++++++ .../Microsoft.ML.AutoML.csproj | 1 + .../netstandard2.0/Microsoft.ML.CpuMath.props | 7 +++++ .../Microsoft.ML.Recommender.csproj | 1 + src/Microsoft.ML/Microsoft.ML.csproj | 1 + 8 files changed, 79 insertions(+), 5 deletions(-) diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index 2660381d2e..ee6cb7f813 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -131,7 +131,30 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x64_Net60 + architecture: arm64 + name: Windows_cross_arm64_Net6 + buildScript: build.cmd + customMatrixes: + Debug_Build: + _configuration: Debug + _config_short: DI + _includeBenchmarkData: false + _targetFramework: net6.0 + Release_Build: + _configuration: Release + _config_short: RI + _includeBenchmarkData: false + _targetFramework: net6.0 + innerLoop: true + vsTestConfiguration: "/Framework:.NETCoreApp,Version=v6.0" + pool: + name: NetCore-Public + demands: ImageOverride -equals 1es-windows-2019-open + helixQueue: Windows.10.Arm64.Open + +- template: /build/ci/job-template.yml + parameters: + name: Windows_x64_Net6 buildScript: build.cmd innerLoop: true vsTestConfiguration: "/Framework:.NETCoreApp,Version=v3.1" @@ -164,7 +187,7 @@ jobs: - template: /build/ci/job-template.yml parameters: - name: Windows_x86_Net60 + name: Windows_x86_Net6 architecture: x86 buildScript: build.cmd innerLoop: true diff --git a/build/vsts-ci.yml b/build/vsts-ci.yml index b3eb3e61d2..9487484d14 100644 --- a/build/vsts-ci.yml +++ b/build/vsts-ci.yml @@ -63,7 +63,7 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish Linux package assets + displayName: Publish Linux_arm package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -86,7 +86,7 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish Linux package assets + displayName: Publish Linux_arm64 package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -133,7 +133,27 @@ stages: displayName: Build Native Assets - task: PublishBuildArtifacts@1 - displayName: Publish macOS package assets + displayName: Publish macOS_M1 package assets + inputs: + pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets + artifactName: pkgassets + artifactType: container + + - template: /eng/common/templates/steps/generate-sbom.yml + + ################################################################################ + - job: Windows_arm64 + ################################################################################ + pool: + name: NetCore1ESPool-Internal + demands: ImageOverride -equals windows.vs2019.amd64 + steps: + # Only build native assets to avoid conflicts. + - script: ./build.cmd -projects $(Build.SourcesDirectory)/src/Native/Native.proj -configuration $(BuildConfig) /p:TargetArchitecture=arm64 /p:CopyPackageAssets=true + displayName: Build Native Assets + + - task: PublishBuildArtifacts@1 + displayName: Publish Windows_arm64 package assets inputs: pathToPublish: $(Build.SourcesDirectory)/artifacts/pkgassets artifactName: pkgassets @@ -141,6 +161,10 @@ stages: - template: /eng/common/templates/steps/generate-sbom.yml + # Terminate all dotnet build processes. + - script: $(Build.SourcesDirectory)/.dotnet/dotnet.exe build-server shutdown + displayName: Dotnet Server Shutdown + ################################################################################ - job: Windows_x86 ################################################################################ diff --git a/eng/pkg/CommonPackage.props b/eng/pkg/CommonPackage.props index 08fbb153af..dddf5970c6 100644 --- a/eng/pkg/CommonPackage.props +++ b/eng/pkg/CommonPackage.props @@ -13,12 +13,20 @@ false %(Filename)%(Extension) + PreserveNewest false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + \ No newline at end of file diff --git a/eng/pkg/Pack.props b/eng/pkg/Pack.props index e436b01c1a..888000e570 100644 --- a/eng/pkg/Pack.props +++ b/eng/pkg/Pack.props @@ -58,12 +58,21 @@ false %(Filename)%(Extension) + PreserveNewest false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + + diff --git a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj index 6ecc501c11..669afe59de 100644 --- a/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj +++ b/src/Microsoft.ML.AutoML/Microsoft.ML.AutoML.csproj @@ -86,6 +86,7 @@ + diff --git a/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props b/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props index ce379e2f00..bdc552e295 100644 --- a/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props +++ b/src/Microsoft.ML.CpuMath/build/netstandard2.0/Microsoft.ML.CpuMath.props @@ -19,6 +19,13 @@ false %(Filename)%(Extension) + + + PreserveNewest + false + %(Filename)%(Extension) + \ No newline at end of file diff --git a/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj b/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj index 92955776e8..980e5719da 100644 --- a/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj +++ b/src/Microsoft.ML.Recommender/Microsoft.ML.Recommender.csproj @@ -26,6 +26,7 @@ + \ No newline at end of file diff --git a/src/Microsoft.ML/Microsoft.ML.csproj b/src/Microsoft.ML/Microsoft.ML.csproj index 780c6048a1..9b8fdf7538 100644 --- a/src/Microsoft.ML/Microsoft.ML.csproj +++ b/src/Microsoft.ML/Microsoft.ML.csproj @@ -66,6 +66,7 @@ + From df0a8dcc3d4b9aeed2da6f554df215f411b2f9c3 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Mon, 11 Sep 2023 16:21:00 -0600 Subject: [PATCH 05/17] fixed MKL arm64 cmake issue --- src/Native/CMakeLists.txt | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/Native/CMakeLists.txt b/src/Native/CMakeLists.txt index 6624e63503..605fe6f700 100644 --- a/src/Native/CMakeLists.txt +++ b/src/Native/CMakeLists.txt @@ -268,6 +268,9 @@ else() if (DEFINED ONEDAL_DEVEL_PATH) message("Path to OneDal library defined [${ONEDAL_DEVEL_PATH}] but it is not being built on this arch.") endif() + if (DEFINED MKL_LIB_PATH) + message("Path to Intel MKL library defined [${MKL_LIB_PATH}] but it is not being built on this arch.") + endif() endif() add_subdirectory(LdaNative) From 91f6569b112b9504984b23a72fe8b13c4058055b Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Wed, 13 Sep 2023 11:16:02 -0600 Subject: [PATCH 06/17] right helix queue --- .vsts-dotnet-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.vsts-dotnet-ci.yml b/.vsts-dotnet-ci.yml index ee6cb7f813..cd05b1802e 100644 --- a/.vsts-dotnet-ci.yml +++ b/.vsts-dotnet-ci.yml @@ -150,7 +150,7 @@ jobs: pool: name: NetCore-Public demands: ImageOverride -equals 1es-windows-2019-open - helixQueue: Windows.10.Arm64.Open + helixQueue: windows.11.arm64.open - template: /build/ci/job-template.yml parameters: From 0939c93e8523f016622aff7d431daced44bd2d8b Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Thu, 9 Nov 2023 04:13:02 -0700 Subject: [PATCH 07/17] fixing arm tests --- test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs index c31cb32506..2492da2047 100644 --- a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs +++ b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs @@ -118,7 +118,7 @@ public void AutoFit_UCI_Adult_Train_Test_Split_Test() Assert.NotNull(result.BestRun.TrainerName); } - [Fact] + [X64Fact("Only x64 is supported.")] public void AutoFit_UCI_Adult_CrossValidation_10_Test() { var context = new MLContext(1); @@ -146,7 +146,7 @@ public void AutoFit_UCI_Adult_CrossValidation_10_Test() Assert.NotNull(model); } - [Fact] + [X64Fact("Only x64 is supported.")] public void AutoFit_Taxi_Fare_Train_Test_Split_Test() { var context = new MLContext(1); @@ -326,7 +326,7 @@ public void AutoFitMultiTest(bool useNumberOfCVFolds) } } - [OnnxFact(Skip = "save space on ci runs")] + [X64Fact("save space on ci runs")] public void AutoFitMultiClassification_Image_TrainTest() { var context = new MLContext(seed: 1); @@ -353,7 +353,7 @@ public void AutoFitMultiClassification_Image_TrainTest() Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type); } - [OnnxFact(Skip = "save space on ci runs")] + [X64Fact("save space on ci runs")] public void AutoFitMultiClassification_Image_CV() { var context = new MLContext(seed: 1); @@ -376,7 +376,7 @@ public void AutoFitMultiClassification_Image_CV() Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type); } - [OnnxFact(Skip = "save space on ci runs")] + [X64Fact("save space on ci runs")] public void AutoFitMultiClassification_Image() { var context = new MLContext(1); From 9927b526c66c99f667245960c9d9af7e2efa732b Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Thu, 9 Nov 2023 11:37:52 -0700 Subject: [PATCH 08/17] makes x64 test detection better --- .../Attributes/X64FactAttribute.cs | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/Microsoft.ML.TestFrameworkCommon/Attributes/X64FactAttribute.cs b/test/Microsoft.ML.TestFrameworkCommon/Attributes/X64FactAttribute.cs index 993b4c29cd..f45dbdd9dd 100644 --- a/test/Microsoft.ML.TestFrameworkCommon/Attributes/X64FactAttribute.cs +++ b/test/Microsoft.ML.TestFrameworkCommon/Attributes/X64FactAttribute.cs @@ -3,6 +3,7 @@ // See the LICENSE file in the project root for more information. using System; +using System.Reflection; namespace Microsoft.ML.TestFramework.Attributes { @@ -18,7 +19,7 @@ public X64FactAttribute(string skipMessage) : base(skipMessage) /// protected override bool IsEnvironmentSupported() { - return Environment.Is64BitProcess; + return Environment.Is64BitProcess && System.Runtime.InteropServices.RuntimeInformation.ProcessArchitecture == System.Runtime.InteropServices.Architecture.X64; } } } From a38569050a08f90f56026f4c1d6d4448424b4cff Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Thu, 9 Nov 2023 12:31:25 -0700 Subject: [PATCH 09/17] change test label --- test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs index 2492da2047..81aeae78f7 100644 --- a/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs +++ b/test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs @@ -326,7 +326,7 @@ public void AutoFitMultiTest(bool useNumberOfCVFolds) } } - [X64Fact("save space on ci runs")] + [OnnxFact(Skip = "save space on ci runs")] public void AutoFitMultiClassification_Image_TrainTest() { var context = new MLContext(seed: 1); @@ -353,7 +353,7 @@ public void AutoFitMultiClassification_Image_TrainTest() Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type); } - [X64Fact("save space on ci runs")] + [OnnxFact(Skip = "save space on ci runs")] public void AutoFitMultiClassification_Image_CV() { var context = new MLContext(seed: 1); @@ -376,7 +376,7 @@ public void AutoFitMultiClassification_Image_CV() Assert.Equal(TextDataViewType.Instance, scoredData.Schema[DefaultColumnNames.PredictedLabel].Type); } - [X64Fact("save space on ci runs")] + [OnnxFact(Skip = "save space on ci runs")] public void AutoFitMultiClassification_Image() { var context = new MLContext(1); From 9ab58dc55d5902af13ccd4ca42dfe04757f3b5e0 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Mon, 13 Nov 2023 21:11:37 -0700 Subject: [PATCH 10/17] fixed onnx files not being included --- test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj b/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj index f6b9a021d9..39073412f3 100644 --- a/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj +++ b/test/Microsoft.ML.AutoML.Tests/Microsoft.ML.AutoML.Tests.csproj @@ -44,7 +44,7 @@ - + DnnImageModels\ResNetPrepOnnx\ResNetPreprocess.onnx PreserveNewest From 563b4de5faaabc7ee317c6a2f4269b0d52391b0f Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Fri, 17 Nov 2023 22:12:49 -0700 Subject: [PATCH 11/17] added new win-arm baselines --- ...veragedPerceptron-CV-breast-cancer-out.txt | 56 ++ ...AveragedPerceptron-CV-breast-cancer-rp.txt | 4 + ...on-CV-breast-cancer.PAVcalibration-out.txt | 58 ++ ...ron-CV-breast-cancer.PAVcalibration-rp.txt | 4 + ...eptron-CV-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++ ...n-CV-breast-cancer.calibrateRandom-out.txt | 56 ++ ...on-CV-breast-cancer.calibrateRandom-rp.txt | 4 + ...ptron-CV-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++ ...ron-CV-breast-cancer.nocalibration-out.txt | 58 ++ ...tron-CV-breast-cancer.nocalibration-rp.txt | 4 + ...ceptron-CV-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++ .../AveragedPerceptron-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...Perceptron-TrainTest-breast-cancer-out.txt | 38 + ...dPerceptron-TrainTest-breast-cancer-rp.txt | 4 + ...nTest-breast-cancer.PAVcalibration-out.txt | 39 + ...inTest-breast-cancer.PAVcalibration-rp.txt | 4 + ...TrainTest-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++ ...Test-breast-cancer.calibrateRandom-out.txt | 38 + ...nTest-breast-cancer.calibrateRandom-rp.txt | 4 + ...rainTest-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++ ...inTest-breast-cancer.nocalibration-out.txt | 39 + ...ainTest-breast-cancer.nocalibration-rp.txt | 4 + ...-TrainTest-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++ ...agedPerceptron-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../CommandTrainMlrWithStats-1-out.txt | 1 + .../win-arm/CommandTrainMlrWithStats-out.txt | 15 + .../CommandTrainMlrWithStats-summary.txt | 18 + .../LDSVM-def-CV-breast-cancer-out.txt | 56 ++ .../win-arm/LDSVM-def-CV-breast-cancer-rp.txt | 4 + .../win-arm/LDSVM-def-CV-breast-cancer.txt | 700 ++++++++++++++++++ .../LDSVM-def-TrainTest-breast-cancer-out.txt | 38 + .../LDSVM-def-TrainTest-breast-cancer-rp.txt | 4 + .../LDSVM-def-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ .../LDSVM-nob-CV-breast-cancer-out.txt | 56 ++ .../win-arm/LDSVM-nob-CV-breast-cancer-rp.txt | 4 + .../win-arm/LDSVM-nob-CV-breast-cancer.txt | 700 ++++++++++++++++++ .../LDSVM-nob-TrainTest-breast-cancer-out.txt | 38 + .../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 4 + .../LDSVM-nob-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ ...ogisticRegression-CV-breast-cancer-out.txt | 112 +++ ...LogisticRegression-CV-breast-cancer-rp.txt | 4 + .../LogisticRegression-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...ion-CV-breast-cancer.withThreshold-out.txt | 112 +++ ...sion-CV-breast-cancer.withThreshold-rp.txt | 4 + ...ression-CV-breast-cancer.withThreshold.txt | 700 ++++++++++++++++++ ...sion-GaussianNorm-CV-breast-cancer-out.txt | 107 +++ ...ssion-GaussianNorm-CV-breast-cancer-rp.txt | 4 + ...gression-GaussianNorm-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...ussianNorm-TrainTest-breast-cancer-out.txt | 64 ++ ...aussianNorm-TrainTest-breast-cancer-rp.txt | 4 + ...n-GaussianNorm-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ ...Regression-TrainTest-breast-cancer-out.txt | 67 ++ ...cRegression-TrainTest-breast-cancer-rp.txt | 4 + ...ession-TrainTest-breast-cancer-summary.txt | 12 + ...sticRegression-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++ ...inTest-breast-cancer.withThreshold-out.txt | 67 ++ ...ainTest-breast-cancer.withThreshold-rp.txt | 4 + ...-TrainTest-breast-cancer.withThreshold.txt | 700 ++++++++++++++++++ ...gression-bin-norm-CV-breast-cancer-out.txt | 99 +++ ...egression-bin-norm-CV-breast-cancer-rp.txt | 4 + ...icRegression-bin-norm-CV-breast-cancer.txt | 700 ++++++++++++++++++ ...n-bin-norm-TrainTest-breast-cancer-out.txt | 62 ++ 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++ .../win-arm/WE-Average-TrainTest-iris-rp.txt | 4 + .../win-arm/WE-Average-TrainTest-iris.txt | 151 ++++ .../WE-Bootstrap-TrainTest-iris-out.txt | 151 ++++ .../WE-Bootstrap-TrainTest-iris-rp.txt | 4 + .../win-arm/WE-Bootstrap-TrainTest-iris.txt | 151 ++++ .../WE-SDCA-Average-TrainTest-iris-out.txt | 71 ++ .../WE-SDCA-Average-TrainTest-iris-rp.txt | 4 + .../WE-SDCA-Average-TrainTest-iris.txt | 151 ++++ .../WE-Stacking-TrainTest-iris-out.txt | 67 ++ .../win-arm/WE-Stacking-TrainTest-iris-rp.txt | 4 + .../win-arm/WE-Stacking-TrainTest-iris.txt | 151 ++++ .../win-arm/WE-Voting-TrainTest-iris-out.txt | 61 ++ .../win-arm/WE-Voting-TrainTest-iris-rp.txt | 4 + .../win-arm/WE-Voting-TrainTest-iris.txt | 151 ++++ .../BaseTestBaseline.cs | 19 +- 128 files changed, 29508 insertions(+), 9 deletions(-) create mode 100644 test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-out.txt create mode 100644 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a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..79e2660b3f --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) +Training calibrator. +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: 0.136411 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.857460 +AUC: 0.994199 +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 || 98 | 7 | 0.9333 + negative || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9703 | 0.9693 | +OVERALL 0/1 ACCURACY: 0.969605 +LOG LOSS/instance: 0.118826 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.868476 +AUC: 0.997577 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995888 (0.0017) +Accuracy: 0.970678 (0.0011) +Positive precision: 0.956577 (0.0137) +Positive recall: 0.959204 (0.0259) +Negative precision: 0.979976 (0.0107) +Negative recall: 0.975122 (0.0115) +Log-loss: 0.127618 (0.0088) +Log-loss reduction: 0.862968 (0.0055) +F1 Score: 0.957480 (0.0060) +AUPRC: 0.992003 (0.0026) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..4d3a5a41cf --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lr /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995888 0.970678 0.956577 0.959204 0.979976 0.975122 0.127618 0.862968 0.95748 0.992003 0.01 100 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=%Output% data=%Data% seed=1 /lr:0.01;/iter:100 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt new file mode 100644 index 0000000000..e598352377 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt @@ -0,0 +1,58 @@ +maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Training calibrator. +PAV calibrator: piecewise function approximation has 5 components. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Training calibrator. +PAV calibrator: piecewise function approximation has 6 components. +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 || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 + ||====================== +Precision || 0.9366 | 0.9953 | +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: 0.994403 +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 || 100 | 5 | 0.9524 + negative || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9709 | 0.9779 | +OVERALL 0/1 ACCURACY: 0.975684 +LOG LOSS/instance: 0.227705 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.747961 +AUC: 0.997619 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) +Log-loss: Infinity (NaN) +Log-loss reduction: -Infinity (NaN) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt new file mode 100644 index 0000000000..95bd831962 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 Infinity -Infinity 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt new file mode 100644 index 0000000000..88cbb23bee --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 11.925824 1 -0 1 +6 0 -0.4527979 0.29372627 0.50170064939900283 0 +8 0 -3.796255 1E-15 1.4415419267167138E-15 0 +9 0 -3.8130417 1E-15 1.4415419267167138E-15 0 +10 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +11 0 -4.606969 1E-15 1.4415419267167138E-15 0 +18 1 7.3475924 1 -0 1 +20 1 6.1389017 1 -0 1 +21 1 7.1486177 1 -0 1 +25 1 1.6632223 0.88235295 0.18057223417631088 1 +28 0 -4.606969 1E-15 1.4415419267167138E-15 0 +31 0 -4.2645645 1E-15 1.4415419267167138E-15 0 +32 1 7.198575 1 -0 1 +35 0 -4.606969 1E-15 1.4415419267167138E-15 0 +37 0 -1.714282 0.1 0.15200309583369792 0 +40 0 ? ? ? 0 +41 1 2.5451746 0.88235295 0.18057223417631088 1 +44 1 8.165841 1 -0 1 +45 0 -4.602255 1E-15 1.4415419267167138E-15 0 +46 1 5.6216097 1 -0 1 +48 0 -3.379683 1E-15 1.4415419267167138E-15 0 +50 1 2.8003244 0.88235295 0.18057223417631088 1 +51 1 0.14775276 0.6666667 0.58496245772549416 1 +52 1 4.696246 1 -0 1 +54 1 6.743868 1 -0 1 +56 1 6.5947094 1 -0 1 +60 1 2.2064123 0.88235295 0.18057223417631088 1 +63 1 0.8789625 0.6666667 0.58496245772549416 1 +64 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +66 0 -3.697434 1E-15 1.4415419267167138E-15 0 +68 1 9.899808 1 -0 1 +69 0 -4.3595524 1E-15 1.4415419267167138E-15 0 +70 0 -3.0557137 1E-15 1.4415419267167138E-15 0 +71 1 7.555621 1 -0 1 +72 0 -1.6769085 0.1 0.15200309583369792 0 +73 1 7.7111273 1 -0 1 +74 1 2.4994192 0.88235295 0.18057223417631088 1 +76 0 -3.8396955 1E-15 1.4415419267167138E-15 0 +77 0 -3.1438046 1E-15 1.4415419267167138E-15 0 +79 0 -4.4265766 1E-15 1.4415419267167138E-15 0 +82 0 -3.1870723 1E-15 1.4415419267167138E-15 0 +88 0 -3.697434 1E-15 1.4415419267167138E-15 0 +90 0 -4.54813 1E-15 1.4415419267167138E-15 0 +91 0 -4.5069323 1E-15 1.4415419267167138E-15 0 +92 0 -3.697434 1E-15 1.4415419267167138E-15 0 +93 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +95 0 -4.54813 1E-15 1.4415419267167138E-15 0 +96 0 -4.790498 1E-15 1.4415419267167138E-15 0 +97 0 -3.413869 1E-15 1.4415419267167138E-15 0 +98 1 9.294541 1 -0 1 +99 1 9.621308 1 -0 1 +100 1 5.1074314 1 -0 1 +102 0 -3.3471546 1E-15 1.4415419267167138E-15 0 +104 1 11.120679 1 -0 1 +105 1 2.1430416 0.88235295 0.18057223417631088 1 +106 1 8.747506 1 -0 1 +108 0 -4.5133796 1E-15 1.4415419267167138E-15 0 +109 1 6.3912544 1 -0 1 +111 1 4.1208715 0.88235295 0.18057223417631088 1 +112 1 7.006652 1 -0 1 +113 1 9.855811 1 -0 1 +115 0 -3.4127908 1E-15 1.4415419267167138E-15 0 +117 1 8.638749 1 -0 1 +120 0 -4.0389752 1E-15 1.4415419267167138E-15 0 +121 0 -3.0156007 1E-15 1.4415419267167138E-15 0 +122 1 10.533509 1 -0 1 +123 1 4.6804914 1 -0 1 +125 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +128 1 4.9510326 1 -0 1 +129 0 -3.789802 1E-15 1.4415419267167138E-15 0 +131 0 -4.2645645 1E-15 1.4415419267167138E-15 0 +132 1 9.206267 1 -0 1 +133 0 -4.1348257 1E-15 1.4415419267167138E-15 0 +137 0 -4.6658077 1E-15 1.4415419267167138E-15 0 +138 0 -3.6895585 1E-15 1.4415419267167138E-15 0 +141 0 -4.9493732 1E-15 1.4415419267167138E-15 0 +144 0 -4.606969 1E-15 1.4415419267167138E-15 0 +145 0 ? ? ? 0 +147 0 -4.2551055 1E-15 1.4415419267167138E-15 0 +150 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +151 1 5.0748987 1 -0 1 +152 1 8.881612 1 -0 1 +154 0 -5.232939 1E-15 1.4415419267167138E-15 0 +156 0 -4.3134584 1E-15 1.4415419267167138E-15 0 +161 0 -3.5088568 1E-15 1.4415419267167138E-15 0 +164 0 ? ? ? 0 +167 1 7.490014 1 -0 1 +169 0 -5.254455 1E-15 1.4415419267167138E-15 0 +171 0 -4.54813 1E-15 1.4415419267167138E-15 0 +173 1 14.494651 1 -0 1 +174 1 5.7514915 1 -0 1 +176 0 -4.2645645 1E-15 1.4415419267167138E-15 0 +177 1 6.138485 1 -0 1 +179 1 2.8749352 0.88235295 0.18057223417631088 1 +180 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +181 0 -5.232939 1E-15 1.4415419267167138E-15 0 +183 1 9.06396 1 -0 1 +187 1 12.00492 1 -0 1 +188 1 8.1324835 1 -0 1 +189 0 -3.6207743 1E-15 1.4415419267167138E-15 0 +191 1 10.797473 1 -0 1 +192 0 -3.7562728 1E-15 1.4415419267167138E-15 0 +196 0 5.8725023 1 Infinity 1 +198 0 -5.232939 1E-15 1.4415419267167138E-15 0 +199 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +201 1 9.770606 1 -0 1 +202 0 -4.54813 1E-15 1.4415419267167138E-15 0 +204 0 -4.54813 1E-15 1.4415419267167138E-15 0 +205 1 12.087651 1 -0 1 +206 1 6.641531 1 -0 1 +207 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +209 0 -3.5942607 1E-15 1.4415419267167138E-15 0 +210 1 13.547517 1 -0 1 +211 1 9.089206 1 -0 1 +212 0 -4.54813 1E-15 1.4415419267167138E-15 0 +216 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +218 1 7.7499733 1 -0 1 +219 0 -2.4297438 1E-15 1.4415419267167138E-15 0 +223 1 5.4305964 1 -0 1 +226 1 9.166205 1 -0 1 +228 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +233 1 5.6998806 1 -0 1 +237 1 6.476473 1 -0 1 +239 1 5.16975 1 -0 1 +240 0 -1.9057708 0.1 0.15200309583369792 0 +241 0 -3.8436408 1E-15 1.4415419267167138E-15 0 +242 0 -4.2645645 1E-15 1.4415419267167138E-15 0 +244 0 -4.54813 1E-15 1.4415419267167138E-15 0 +246 1 11.068809 1 -0 1 +247 1 3.1855068 0.88235295 0.18057223417631088 1 +248 0 -2.7545462 1E-15 1.4415419267167138E-15 0 +249 0 ? ? ? 0 +250 0 -4.655863 1E-15 1.4415419267167138E-15 0 +252 0 4.2659445 0.96023005 4.6521775310747371 1 +254 1 7.919628 1 -0 1 +257 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +258 0 -3.9809995 1E-15 1.4415419267167138E-15 0 +259 0 4.3407545 1 Infinity 1 +260 1 8.330071 1 -0 1 +262 1 9.629434 1 -0 1 +267 1 3.677929 0.88235295 0.18057223417631088 1 +268 1 8.609313 1 -0 1 +269 0 -4.54813 1E-15 1.4415419267167138E-15 0 +271 0 -3.413869 1E-15 1.4415419267167138E-15 0 +272 1 3.677929 0.88235295 0.18057223417631088 1 +275 0 ? ? ? 0 +276 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +277 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +278 0 -4.54813 1E-15 1.4415419267167138E-15 0 +279 1 6.636299 1 -0 1 +280 0 -3.9809995 1E-15 1.4415419267167138E-15 0 +283 1 5.5930414 1 -0 1 +284 1 6.3683033 1 -0 1 +285 1 13.342931 1 -0 1 +288 1 2.133194 0.88235295 0.18057223417631088 1 +290 0 -5.232939 1E-15 1.4415419267167138E-15 0 +291 0 -4.54813 1E-15 1.4415419267167138E-15 0 +293 1 4.659646 1 -0 1 +296 0 2.0631151 0.88235295 3.0874629272416674 1 +297 0 ? ? ? 0 +299 1 6.110528 1 -0 1 +300 1 6.6979437 1 -0 1 +301 0 -4.54813 1E-15 1.4415419267167138E-15 0 +303 0 -4.54813 1E-15 1.4415419267167138E-15 0 +304 1 4.865142 1 -0 1 +308 1 6.804653 1 -0 1 +309 0 -1.4760427 0.1 0.15200309583369792 0 +311 0 -5.232939 1E-15 1.4415419267167138E-15 0 +312 1 3.2489738 0.88235295 0.18057223417631088 1 +314 0 -5.070926 1E-15 1.4415419267167138E-15 0 +316 1 4.409379 1 -0 1 +317 1 8.798545 1 -0 1 +319 0 2.7989082 0.88235295 3.0874629272416674 1 +321 0 ? ? ? 0 +323 1 4.8855352 1 -0 1 +327 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +328 1 4.1213074 0.88235295 0.18057223417631088 1 +329 1 6.918497 1 -0 1 +331 0 -3.1255894 1E-15 1.4415419267167138E-15 0 +332 0 -2.8317442 1E-15 1.4415419267167138E-15 0 +333 1 4.9879713 1 -0 1 +336 1 5.3119774 1 -0 1 +338 0 -5.070926 1E-15 1.4415419267167138E-15 0 +343 0 -5.232939 1E-15 1.4415419267167138E-15 0 +344 1 9.373975 1 -0 1 +346 0 -2.8051786 1E-15 1.4415419267167138E-15 0 +347 0 -5.0361757 1E-15 1.4415419267167138E-15 0 +348 1 0.09843826 0.6666667 0.58496245772549416 1 +349 1 3.543579 0.88235295 0.18057223417631088 1 +350 0 -3.7809262 1E-15 1.4415419267167138E-15 0 +352 0 1.515585 0.88235295 3.0874629272416674 1 +353 1 9.391767 1 -0 1 +354 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +355 0 -3.6708689 1E-15 1.4415419267167138E-15 0 +358 1 6.2604895 1 -0 1 +360 1 15.035466 1 -0 1 +361 1 6.5571547 1 -0 1 +366 1 13.467437 1 -0 1 +368 0 -4.532379 1E-15 1.4415419267167138E-15 0 +370 0 -3.0555735 1E-15 1.4415419267167138E-15 0 +371 0 -4.532379 1E-15 1.4415419267167138E-15 0 +373 0 -3.5973973 1E-15 1.4415419267167138E-15 0 +376 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +377 0 -5.070926 1E-15 1.4415419267167138E-15 0 +378 0 -3.477808 1E-15 1.4415419267167138E-15 0 +379 0 -1.5486526 0.1 0.15200309583369792 0 +381 1 8.643491 1 -0 1 +383 0 -4.9493732 1E-15 1.4415419267167138E-15 0 +384 0 -4.9493732 1E-15 1.4415419267167138E-15 0 +387 0 -1.9204493 0.1 0.15200309583369792 0 +388 0 -4.418391 1E-15 1.4415419267167138E-15 0 +389 0 -2.9322662 1E-15 1.4415419267167138E-15 0 +391 1 9.323798 1 -0 1 +392 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +395 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +396 0 -3.9809995 1E-15 1.4415419267167138E-15 0 +398 0 -3.9080298 1E-15 1.4415419267167138E-15 0 +399 0 -4.305583 1E-15 1.4415419267167138E-15 0 +404 0 -4.499814 1E-15 1.4415419267167138E-15 0 +406 0 -3.4627624 1E-15 1.4415419267167138E-15 0 +409 0 -3.973124 1E-15 1.4415419267167138E-15 0 +413 0 -3.063589 1E-15 1.4415419267167138E-15 0 +414 1 6.5001116 1 -0 1 +415 0 -0.5379734 0.15984297 0.25126909064706171 0 +416 1 8.981729 1 -0 1 +418 0 -1.7460232 0.1 0.15200309583369792 0 +419 0 -3.9546041 1E-15 1.4415419267167138E-15 0 +422 0 -2.1661267 1E-15 1.4415419267167138E-15 0 +423 0 -3.0557137 1E-15 1.4415419267167138E-15 0 +428 0 -4.8905344 1E-15 1.4415419267167138E-15 0 +429 0 -4.606969 1E-15 1.4415419267167138E-15 0 +430 0 -4.2483644 1E-15 1.4415419267167138E-15 0 +434 0 5.678664 1 Infinity 1 +436 1 4.8968487 1 -0 1 +439 0 -4.098677 1E-15 1.4415419267167138E-15 0 +440 1 7.6677313 1 -0 1 +441 0 -1.6798639 0.1 0.15200309583369792 0 +442 0 -4.0051136 1E-15 1.4415419267167138E-15 0 +449 1 10.087396 1 -0 1 +450 0 -3.9024792 1E-15 1.4415419267167138E-15 0 +451 0 -4.098677 1E-15 1.4415419267167138E-15 0 +452 0 -4.348057 1E-15 1.4415419267167138E-15 0 +453 1 8.068193 1 -0 1 +454 0 -4.3153887 1E-15 1.4415419267167138E-15 0 +455 1 0.4596901 0.6666667 0.58496245772549416 1 +456 1 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1E-15 1.4415419267167138E-15 0 +521 0 -4.2018504 1E-15 1.4415419267167138E-15 0 +522 1 4.3094044 0.9868354 0.019118596181580929 1 +523 1 6.7598047 1 -0 1 +527 0 -3.697434 1E-15 1.4415419267167138E-15 0 +528 0 -2.955677 1E-15 1.4415419267167138E-15 0 +529 0 -4.22023 1E-15 1.4415419267167138E-15 0 +531 0 -3.4627624 1E-15 1.4415419267167138E-15 0 +532 0 -4.7285223 1E-15 1.4415419267167138E-15 0 +533 0 -4.3234034 1E-15 1.4415419267167138E-15 0 +534 0 -4.606969 1E-15 1.4415419267167138E-15 0 +535 0 -3.6886954 1E-15 1.4415419267167138E-15 0 +538 0 -4.0398383 1E-15 1.4415419267167138E-15 0 +539 0 -3.4727077 1E-15 1.4415419267167138E-15 0 +540 0 -3.3012362 1E-15 1.4415419267167138E-15 0 +541 0 -4.6658077 1E-15 1.4415419267167138E-15 0 +544 0 -3.7475343 1E-15 1.4415419267167138E-15 0 +546 1 11.016192 1 -0 1 +547 0 -5.1297655 1E-15 1.4415419267167138E-15 0 +548 0 -4.8462 1E-15 1.4415419267167138E-15 0 +549 1 5.5535192 1 -0 1 +557 0 -3.7809262 1E-15 1.4415419267167138E-15 0 +558 0 -4.606969 1E-15 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-4.921291 1E-15 1.4415419267167138E-15 0 +673 0 -3.289723 1E-15 1.4415419267167138E-15 0 +674 0 -5.669884 1E-15 1.4415419267167138E-15 0 +675 0 -3.7340279 1E-15 1.4415419267167138E-15 0 +676 0 -5.419147 1E-15 1.4415419267167138E-15 0 +677 0 -4.0031805 1E-15 1.4415419267167138E-15 0 +684 0 -5.935418 1E-15 1.4415419267167138E-15 0 +686 0 -5.935418 1E-15 1.4415419267167138E-15 0 +687 0 -4.295347 1E-15 1.4415419267167138E-15 0 +690 0 -5.4890733 1E-15 1.4415419267167138E-15 0 +695 0 -5.4523587 1E-15 1.4415419267167138E-15 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt new file mode 100644 index 0000000000..7d3ac2552a --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Training calibrator. +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 || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 + ||====================== +Precision || 0.9366 | 0.9953 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: 0.139629 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.854097 +AUC: 0.994403 +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 || 100 | 5 | 0.9524 + negative || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9709 | 0.9779 | +OVERALL 0/1 ACCURACY: 0.975684 +LOG LOSS/instance: 0.121001 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.866069 +AUC: 0.997619 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) +Log-loss: 0.130315 (0.0093) +Log-loss reduction: 0.860083 (0.0060) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt new file mode 100644 index 0000000000..ae8060865c --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 0.130315 0.860083 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt new file mode 100644 index 0000000000..68b75e3a8b --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 11.925824 0.99988866 0.00016064073906035849 1 +6 0 -0.4527979 0.28799012 0.49003084056125074 0 +8 0 -3.796255 0.026382547 0.038573064761285226 0 +9 0 -3.8130417 0.026036168 0.038059895132779242 0 +10 0 -4.7285223 0.0125917215 0.018281354087045291 0 +11 0 -4.606969 0.013873976 0.02015606392328537 0 +18 1 7.3475924 0.99550986 0.006492485535140746 1 +20 1 6.1389017 0.9881576 0.017186984398807038 1 +21 1 7.1486177 0.9947303 0.0076226814052998944 1 +25 1 1.6632223 0.6911742 0.53287870995530984 1 +28 0 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-5.4523587 0.00905354 0.013120983569838577 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt new file mode 100644 index 0000000000..45cc4803b0 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt @@ -0,0 +1,58 @@ +maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Not training a calibrator because a valid calibrator trainer was not provided. +Warning: Data does not contain a probability column. Will not output the Log-loss column +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) +Not training a calibrator because a valid calibrator trainer was not provided. +Warning: Data does not contain a probability column. Will not output the Log-loss column +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 || 133 | 1 | 0.9925 + negative || 9 | 211 | 0.9591 + ||====================== +Precision || 0.9366 | 0.9953 | +OVERALL 0/1 ACCURACY: 0.971751 +LOG LOSS/instance: NaN +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.994403 +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 || 100 | 5 | 0.9524 + negative || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9709 | 0.9779 | +OVERALL 0/1 ACCURACY: 0.975684 +LOG LOSS/instance: NaN +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.997619 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996011 (0.0016) +Accuracy: 0.973718 (0.0020) +Positive precision: 0.953747 (0.0171) +Positive recall: 0.972459 (0.0201) +Negative precision: 0.986580 (0.0087) +Negative recall: 0.972849 (0.0138) +Log-loss: NaN (NaN) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: 0.962653 (0.0011) +AUPRC: 0.992269 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt new file mode 100644 index 0000000000..9e9d966393 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996011 0.973718 0.953747 0.972459 0.98658 0.972849 NaN 0 0.962653 0.992269 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration.txt new file mode 100644 index 0000000000..05456092a2 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.nocalibration.txt @@ -0,0 +1,700 @@ +Instance Label Score Assigned +5 1 11.925824 1 +6 0 -0.4527979 0 +8 0 -3.796255 0 +9 0 -3.8130417 0 +10 0 -4.7285223 0 +11 0 -4.606969 0 +18 1 7.3475924 1 +20 1 6.1389017 1 +21 1 7.1486177 1 +25 1 1.6632223 1 +28 0 -4.606969 0 +31 0 -4.2645645 0 +32 1 7.198575 1 +35 0 -4.606969 0 +37 0 -1.714282 0 +40 0 ? 0 +41 1 2.5451746 1 +44 1 8.165841 1 +45 0 -4.602255 0 +46 1 5.6216097 1 +48 0 -3.379683 0 +50 1 2.8003244 1 +51 1 0.14775276 1 +52 1 4.696246 1 +54 1 6.743868 1 +56 1 6.5947094 1 +60 1 2.2064123 1 +63 1 0.8789625 1 +64 0 -4.8905344 0 +66 0 -3.697434 0 +68 1 9.899808 1 +69 0 -4.3595524 0 +70 0 -3.0557137 0 +71 1 7.555621 1 +72 0 -1.6769085 0 +73 1 7.7111273 1 +74 1 2.4994192 1 +76 0 -3.8396955 0 +77 0 -3.1438046 0 +79 0 -4.4265766 0 +82 0 -3.1870723 0 +88 0 -3.697434 0 +90 0 -4.54813 0 +91 0 -4.5069323 0 +92 0 -3.697434 0 +93 0 -4.8905344 0 +95 0 -4.54813 0 +96 0 -4.790498 0 +97 0 -3.413869 0 +98 1 9.294541 1 +99 1 9.621308 1 +100 1 5.1074314 1 +102 0 -3.3471546 0 +104 1 11.120679 1 +105 1 2.1430416 1 +106 1 8.747506 1 +108 0 -4.5133796 0 +109 1 6.3912544 1 +111 1 4.1208715 1 +112 1 7.006652 1 +113 1 9.855811 1 +115 0 -3.4127908 0 +117 1 8.638749 1 +120 0 -4.0389752 0 +121 0 -3.0156007 0 +122 1 10.533509 1 +123 1 4.6804914 1 +125 0 -4.8905344 0 +128 1 4.9510326 1 +129 0 -3.789802 0 +131 0 -4.2645645 0 +132 1 9.206267 1 +133 0 -4.1348257 0 +137 0 -4.6658077 0 +138 0 -3.6895585 0 +141 0 -4.9493732 0 +144 0 -4.606969 0 +145 0 ? 0 +147 0 -4.2551055 0 +150 0 -4.7285223 0 +151 1 5.0748987 1 +152 1 8.881612 1 +154 0 -5.232939 0 +156 0 -4.3134584 0 +161 0 -3.5088568 0 +164 0 ? 0 +167 1 7.490014 1 +169 0 -5.254455 0 +171 0 -4.54813 0 +173 1 14.494651 1 +174 1 5.7514915 1 +176 0 -4.2645645 0 +177 1 6.138485 1 +179 1 2.8749352 1 +180 0 -4.7285223 0 +181 0 -5.232939 0 +183 1 9.06396 1 +187 1 12.00492 1 +188 1 8.1324835 1 +189 0 -3.6207743 0 +191 1 10.797473 1 +192 0 -3.7562728 0 +196 0 5.8725023 1 +198 0 -5.232939 0 +199 0 -4.3234034 0 +201 1 9.770606 1 +202 0 -4.54813 0 +204 0 -4.54813 0 +205 1 12.087651 1 +206 1 6.641531 1 +207 0 -4.7285223 0 +209 0 -3.5942607 0 +210 1 13.547517 1 +211 1 9.089206 1 +212 0 -4.54813 0 +216 0 -4.8905344 0 +218 1 7.7499733 1 +219 0 -2.4297438 0 +223 1 5.4305964 1 +226 1 9.166205 1 +228 0 -4.7285223 0 +233 1 5.6998806 1 +237 1 6.476473 1 +239 1 5.16975 1 +240 0 -1.9057708 0 +241 0 -3.8436408 0 +242 0 -4.2645645 0 +244 0 -4.54813 0 +246 1 11.068809 1 +247 1 3.1855068 1 +248 0 -2.7545462 0 +249 0 ? 0 +250 0 -4.655863 0 +252 0 4.2659445 1 +254 1 7.919628 1 +257 0 -4.3234034 0 +258 0 -3.9809995 0 +259 0 4.3407545 1 +260 1 8.330071 1 +262 1 9.629434 1 +267 1 3.677929 1 +268 1 8.609313 1 +269 0 -4.54813 0 +271 0 -3.413869 0 +272 1 3.677929 1 +275 0 ? 0 +276 0 -4.3234034 0 +277 0 -4.8905344 0 +278 0 -4.54813 0 +279 1 6.636299 1 +280 0 -3.9809995 0 +283 1 5.5930414 1 +284 1 6.3683033 1 +285 1 13.342931 1 +288 1 2.133194 1 +290 0 -5.232939 0 +291 0 -4.54813 0 +293 1 4.659646 1 +296 0 2.0631151 1 +297 0 ? 0 +299 1 6.110528 1 +300 1 6.6979437 1 +301 0 -4.54813 0 +303 0 -4.54813 0 +304 1 4.865142 1 +308 1 6.804653 1 +309 0 -1.4760427 0 +311 0 -5.232939 0 +312 1 3.2489738 1 +314 0 -5.070926 0 +316 1 4.409379 1 +317 1 8.798545 1 +319 0 2.7989082 1 +321 0 ? 0 +323 1 4.8855352 1 +327 0 -4.8905344 0 +328 1 4.1213074 1 +329 1 6.918497 1 +331 0 -3.1255894 0 +332 0 -2.8317442 0 +333 1 4.9879713 1 +336 1 5.3119774 1 +338 0 -5.070926 0 +343 0 -5.232939 0 +344 1 9.373975 1 +346 0 -2.8051786 0 +347 0 -5.0361757 0 +348 1 0.09843826 1 +349 1 3.543579 1 +350 0 -3.7809262 0 +352 0 1.515585 1 +353 1 9.391767 1 +354 0 -4.8905344 0 +355 0 -3.6708689 0 +358 1 6.2604895 1 +360 1 15.035466 1 +361 1 6.5571547 1 +366 1 13.467437 1 +368 0 -4.532379 0 +370 0 -3.0555735 0 +371 0 -4.532379 0 +373 0 -3.5973973 0 +376 0 -4.8905344 0 +377 0 -5.070926 0 +378 0 -3.477808 0 +379 0 -1.5486526 0 +381 1 8.643491 1 +383 0 -4.9493732 0 +384 0 -4.9493732 0 +387 0 -1.9204493 0 +388 0 -4.418391 0 +389 0 -2.9322662 0 +391 1 9.323798 1 +392 0 -4.3234034 0 +395 0 -4.3234034 0 +396 0 -3.9809995 0 +398 0 -3.9080298 0 +399 0 -4.305583 0 +404 0 -4.499814 0 +406 0 -3.4627624 0 +409 0 -3.973124 0 +413 0 -3.063589 0 +414 1 6.5001116 1 +415 0 -0.5379734 0 +416 1 8.981729 1 +418 0 -1.7460232 0 +419 0 -3.9546041 0 +422 0 -2.1661267 0 +423 0 -3.0557137 0 +428 0 -4.8905344 0 +429 0 -4.606969 0 +430 0 -4.2483644 0 +434 0 5.678664 1 +436 1 4.8968487 1 +439 0 -4.098677 0 +440 1 7.6677313 1 +441 0 -1.6798639 0 +442 0 -4.0051136 0 +449 1 10.087396 1 +450 0 -3.9024792 0 +451 0 -4.098677 0 +452 0 -4.348057 0 +453 1 8.068193 1 +454 0 -4.3153887 0 +455 1 0.4596901 1 +456 1 8.942605 1 +457 1 8.346686 1 +464 0 -4.3822427 0 +465 1 8.953591 1 +466 1 8.690221 1 +467 1 7.4474792 1 +474 0 -4.098677 0 +480 0 -4.2790694 0 +482 1 14.295785 1 +483 1 10.273198 1 +484 0 -3.7483978 0 +487 1 12.309564 1 +489 1 -0.4385233 0 +492 0 -4.22023 0 +493 1 10.096398 1 +495 0 -4.5037956 0 +497 0 -4.155446 0 +501 0 -4.0398383 0 +502 0 -3.8809628 0 +504 0 -5.232939 0 +507 0 -3.867828 0 +510 0 -5.232939 0 +513 0 -4.5037956 0 +514 1 9.508458 1 +517 0 -5.070926 0 +519 1 6.943595 1 +520 0 -5.0525465 0 +521 0 -4.2018504 0 +522 1 4.3094044 1 +523 1 6.7598047 1 +527 0 -3.697434 0 +528 0 -2.955677 0 +529 0 -4.22023 0 +531 0 -3.4627624 0 +532 0 -4.7285223 0 +533 0 -4.3234034 0 +534 0 -4.606969 0 +535 0 -3.6886954 0 +538 0 -4.0398383 0 +539 0 -3.4727077 0 +540 0 -3.3012362 0 +541 0 -4.6658077 0 +544 0 -3.7475343 0 +546 1 11.016192 1 +547 0 -5.1297655 0 +548 0 -4.8462 0 +549 1 5.5535192 1 +557 0 -3.7809262 0 +558 0 -4.606969 0 +559 0 -3.7562728 0 +560 0 -3.413869 0 +561 0 -3.413869 0 +563 0 -4.3234034 0 +565 1 11.259841 1 +566 0 -3.6307197 0 +569 1 9.242663 1 +577 0 -4.8905344 0 +578 0 -4.8905344 0 +581 1 8.654239 1 +582 1 8.128011 1 +584 0 -3.123908 0 +586 1 12.971318 1 +590 1 4.1888075 1 +593 0 -3.7483978 0 +594 1 5.7850237 1 +600 0 -4.3234034 0 +602 0 -4.0398383 0 +604 1 5.138942 1 +606 0 -4.223367 0 +607 0 -5.232939 0 +609 0 -4.098677 0 +612 1 15.565835 1 +613 0 -4.153376 0 +614 0 -4.787361 0 +617 0 ? 0 +618 0 -4.0398383 0 +619 0 -3.7562728 0 +621 0 0.0042419434 1 +622 0 -2.5211115 0 +624 0 -3.7777896 0 +627 0 -3.1292257 0 +629 0 -4.3822427 0 +633 1 4.158328 1 +634 0 -4.6658077 0 +638 0 -4.3822427 0 +639 0 -3.7809262 0 +641 0 -4.3234034 0 +642 0 -4.3234034 0 +644 0 -4.9493732 0 +645 0 -4.3234034 0 +649 0 -4.3234034 0 +652 0 -3.5676956 0 +653 0 -4.0398383 0 +654 0 -3.9809995 0 +656 0 -3.7562728 0 +657 0 0.6928787 1 +660 0 -4.8905344 0 +661 0 -3.697434 0 +665 0 -5.232939 0 +668 1 3.362691 1 +670 1 6.9357576 1 +678 0 -5.232939 0 +679 0 -4.9493732 0 +680 1 15.087735 1 +681 1 9.870067 1 +682 0 -3.2252913 0 +683 0 -5.232939 0 +685 0 -5.232939 0 +688 0 -4.3822427 0 +689 0 -3.214981 0 +691 1 5.504386 1 +692 0 -4.6658077 0 +693 0 -4.035124 0 +694 0 -4.030379 0 +696 1 7.6071005 1 +697 1 5.0033855 1 +698 1 6.1210938 1 +0 0 -3.4721127 0 +1 0 2.4163914 1 +2 0 -4.045404 0 +3 0 2.9251795 1 +4 0 -3.5088272 0 +7 0 -4.6705537 0 +12 1 -0.34343147 0 +13 0 -4.618695 0 +14 1 7.3602133 1 +15 1 0.6494303 1 +16 0 -4.220706 0 +17 0 -3.955172 0 +19 0 -2.9890532 0 +22 0 -4.7037654 0 +23 1 ? 0 +24 0 -5.4043503 0 +26 0 -4.390918 0 +27 0 -3.7376466 0 +29 0 -5.4339433 0 +30 0 -4.649441 0 +33 0 -4.6981187 0 +34 0 -4.453028 0 +36 1 7.832773 1 +38 1 4.928609 1 +39 1 1.079258 1 +42 1 6.8985863 1 +43 1 -0.49528694 0 +47 0 -5.669884 0 +49 1 5.3024063 1 +53 1 5.116103 1 +55 1 4.4195347 1 +57 1 0.5701313 1 +58 1 1.1371031 1 +59 1 1.6442327 1 +61 0 -5.277056 0 +62 1 5.7670774 1 +65 1 2.7867746 1 +67 1 2.9058533 1 +75 0 -4.2910423 0 +78 0 -3.652576 0 +80 0 -3.2955704 0 +81 0 -3.9975338 0 +83 0 -2.9832683 0 +84 1 6.5823994 1 +85 1 4.7604074 1 +86 1 1.466999 1 +87 1 5.223544 1 +89 0 -5.017977 0 +94 0 -4.921291 0 +101 1 -0.85990334 0 +103 1 0.36755466 1 +107 1 4.617798 1 +110 0 -3.1312823 0 +114 0 -2.8847933 0 +116 0 -0.40737772 0 +118 0 -5.258412 0 +119 0 -3.7868814 0 +124 1 5.0384197 1 +126 1 6.4765244 1 +127 0 -4.4382315 0 +130 0 -3.2361717 0 +134 0 -4.75809 0 +135 0 -2.670867 0 +136 0 -4.220706 0 +139 0 ? 0 +140 0 -4.9692993 0 +142 1 3.7043686 1 +143 0 -4.364107 0 +146 1 0.3614874 1 +148 0 -2.3573046 0 +149 1 8.756336 1 +153 0 -3.7005844 0 +155 1 3.1619139 1 +157 0 -4.921291 0 +158 0 ? 0 +159 1 10.452137 1 +160 1 7.997595 1 +162 0 -4.4382315 0 +163 0 -3.6690454 0 +165 0 -3.3581352 0 +166 1 6.3355865 1 +168 0 -4.4382315 0 +170 0 -4.9692993 0 +172 0 -5.669884 0 +175 1 6.0381765 1 +178 0 -3.955172 0 +182 0 -2.9890532 0 +184 1 5.409272 1 +185 0 -4.8669662 0 +186 1 3.9876003 1 +190 1 10.521242 1 +193 0 -5.4043503 0 +194 0 -4.4382315 0 +195 0 -3.955172 0 +197 0 -2.8025956 0 +200 1 8.673523 1 +203 0 -3.4721127 0 +208 0 -5.3500257 0 +213 1 12.300528 1 +214 1 11.895983 1 +215 1 6.600219 1 +217 0 -5.4043503 0 +220 0 -5.181178 0 +221 1 7.9662914 1 +222 1 -2.1487255 0 +224 1 8.4735565 1 +225 0 -5.669884 0 +227 1 6.748211 1 +229 1 10.504805 1 +230 1 4.829337 1 +231 1 6.912092 1 +232 0 1.0722923 1 +234 0 -2.7037287 0 +235 0 ? 0 +236 1 9.440506 1 +238 1 10.690645 1 +243 0 -3.3019714 0 +245 0 -2.8561125 0 +251 1 7.355525 1 +253 1 6.8985863 1 +255 1 3.7452059 1 +256 0 -4.9692993 0 +261 1 9.009869 1 +263 1 7.14557 1 +264 1 4.0145664 1 +265 0 -2.5156918 0 +266 1 7.325534 1 +270 1 5.5723915 1 +273 1 0.037317276 1 +274 0 -4.2340226 0 +281 0 -4.6981187 0 +282 1 2.860157 1 +286 1 12.544172 1 +287 0 -4.75809 0 +289 1 6.6595707 1 +292 1 ? 0 +294 0 ? 0 +295 1 5.621521 1 +298 0 -2.4584541 0 +302 1 12.725582 1 +305 1 8.040863 1 +306 0 -5.4043503 0 +307 0 -5.4043503 0 +310 0 -5.2411494 0 +313 0 -5.935418 0 +315 0 ? 0 +318 0 -5.567325 0 +320 1 5.5611877 1 +322 0 -4.4382315 0 +324 0 -5.4043503 0 +325 0 -3.7860875 0 +326 1 3.6223297 1 +330 1 4.9927444 1 +334 1 5.514736 1 +335 0 -5.935418 0 +337 0 -5.4043503 0 +339 1 5.1161976 1 +340 1 5.5803356 1 +341 0 -5.4043503 0 +342 0 -5.4523587 0 +345 0 -5.935418 0 +351 0 -4.921291 0 +356 1 -0.9127703 0 +357 1 9.874601 1 +359 1 4.653247 1 +362 0 -3.5174994 0 +363 0 -1.6840982 0 +364 0 -4.921291 0 +365 0 -5.186825 0 +367 1 9.040358 1 +369 0 -5.1140847 0 +372 0 -3.9699688 0 +374 0 -4.453028 0 +375 0 -5.935418 0 +380 0 -5.935418 0 +382 0 -3.5015903 0 +385 0 -3.4273863 0 +386 1 5.084236 1 +390 0 -5.4467115 0 +393 0 -5.8810935 0 +394 0 -4.909887 0 +397 0 -4.48624 0 +400 1 7.3133698 1 +401 0 -4.9692993 0 +402 0 -2.7191267 0 +403 0 -3.7908158 0 +405 0 -5.669884 0 +407 0 -5.669884 0 +408 0 -3.537508 0 +410 0 -5.669884 0 +411 0 ? 0 +412 1 7.6394253 1 +417 0 -5.669884 0 +420 0 -2.696971 0 +421 1 9.498289 1 +424 0 -4.9692993 0 +425 1 11.849485 1 +426 0 -2.2324486 0 +427 1 4.1596613 1 +431 0 -2.9302087 0 +432 0 -3.5334377 0 +433 0 -4.013695 0 +435 1 7.0216722 1 +437 0 -4.48624 0 +438 0 -3.5384207 0 +443 0 -5.54259 0 +444 0 -2.6761098 0 +445 0 -5.4523587 0 +446 0 -5.935418 0 +447 0 -4.0031805 0 +448 0 -5.8810935 0 +458 0 -3.7524428 0 +459 0 -3.5017056 0 +460 0 -3.5568361 0 +461 0 -3.3979883 0 +462 0 -3.0737762 0 +463 0 -4.282031 0 +468 0 -4.48624 0 +469 0 -5.419147 0 +470 0 -4.649441 0 +471 0 -3.0737762 0 +472 0 -3.4657965 0 +473 0 -4.48624 0 +475 0 -4.9692993 0 +476 0 -4.2355022 0 +477 0 -4.48624 0 +478 0 -3.744658 0 +479 1 6.673234 1 +481 0 -2.5658808 0 +485 0 -4.6490927 0 +486 0 -4.649441 0 +488 1 0.95910263 1 +490 0 -5.935418 0 +491 1 5.556222 1 +494 0 -0.01569748 0 +496 0 -5.8810935 0 +498 0 -4.220706 0 +499 0 -4.220706 0 +500 0 -2.9890532 0 +503 0 -3.955172 0 +505 0 -4.4522543 0 +506 1 8.766859 1 +508 0 -4.0031805 0 +509 0 -5.4523587 0 +511 0 -3.7376466 0 +512 0 -4.0031805 0 +515 1 7.3749876 1 +516 0 -5.8810935 0 +518 0 -4.49539 0 +524 0 -4.7037654 0 +525 0 -4.746127 0 +526 0 -4.48624 0 +530 1 5.325205 1 +536 0 -3.4721127 0 +537 0 -3.2213755 0 +542 0 -3.4861355 0 +543 0 -4.220706 0 +545 0 -3.7376466 0 +550 0 -4.7037654 0 +551 0 -5.4043503 0 +552 0 -3.2360563 0 +553 0 -1.4225531 0 +554 0 -4.9692993 0 +555 0 -1.7431297 0 +556 0 -2.9508896 0 +562 0 -5.4043503 0 +564 0 -3.7509632 0 +567 0 -3.4350505 0 +568 1 3.5748358 1 +570 1 6.466877 1 +571 1 9.048693 1 +572 0 -4.7037654 0 +573 0 -5.669884 0 +574 1 5.533701 1 +575 0 -3.2213755 0 +576 0 -3.7376466 0 +579 0 -5.4043503 0 +580 0 -3.4869094 0 +583 0 -4.9692993 0 +585 0 -5.935418 0 +587 0 -3.5334377 0 +588 1 4.6442146 1 +589 0 -4.0031805 0 +591 1 4.243067 1 +592 1 4.8517904 1 +595 0 -3.7376466 0 +596 0 -3.9699688 0 +597 0 -2.9706378 0 +598 0 -4.7037654 0 +599 0 -2.9381208 0 +601 0 -5.6155596 0 +603 1 3.1762505 1 +605 1 8.159748 1 +608 1 8.079367 1 +610 1 6.972576 1 +611 1 5.494137 1 +615 0 -3.7192311 0 +616 0 -4.7037654 0 +620 0 -4.7037654 0 +623 0 -5.935418 0 +625 0 -3.343666 0 +626 1 3.8647957 1 +628 0 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b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 4.200075 0.9999651 5.0391796758373727E-05 1 +6 0 -0.30853248 0.2536451 0.42206626883841641 0 +8 0 -1.70133 0.010123582 0.014679672449874221 0 +9 0 -1.5288423 0.01553754 0.022591901886398588 0 +10 0 -1.9129735 0.005969637 0.0086381749163380664 0 +11 0 -1.8355699 0.007243505 0.010488200743506116 0 +18 1 2.5823843 0.99795955 0.0029467484071289771 1 +20 1 2.114814 0.9934155 0.0095308744102067192 1 +21 1 2.3608449 0.9964431 0.0051406802358066545 1 +25 1 0.44596696 0.69394016 0.5271168279878311 1 +28 0 -1.8355699 0.007243505 0.010488200743506116 0 +31 0 -1.7324895 0.009367555 0.013578222828773317 0 +32 1 2.35438 0.996385 0.0052248235129257635 1 +35 0 -1.8355699 0.007243505 0.010488200743506116 0 +37 0 -0.88281894 0.07420459 0.11123467943805206 0 +40 0 ? ? ? 0 +41 1 0.6366718 0.7855495 0.34822586826299018 1 +44 1 2.7207205 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-1.9600375 0.006218537 0.0089994636539313857 0 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..1ff697ed13 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter) +Training calibrator. +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 || 232 | 7 | 0.9707 + negative || 12 | 432 | 0.9730 + ||====================== +Precision || 0.9508 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.972182 +LOG LOSS/instance: 0.115962 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875844 +AUC: 0.995995 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995995 (0.0000) +Accuracy: 0.972182 (0.0000) +Positive precision: 0.950820 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984055 (0.0000) +Negative recall: 0.972973 (0.0000) +Log-loss: 0.115962 (0.0000) +Log-loss reduction: 0.875844 (0.0000) +F1 Score: 0.960663 (0.0000) +AUPRC: 0.991840 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..efcbfb47b1 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /lr /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995995 0.972182 0.95082 0.970711 0.984055 0.972973 0.115962 0.875844 0.960663 0.99184 0.01 100 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=%Output% data=%Data% out=%Output% seed=1 /lr:0.01;/iter:100 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt new file mode 100644 index 0000000000..08ac6f6825 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt @@ -0,0 +1,39 @@ +maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) +Training calibrator. +PAV calibrator: piecewise function approximation has 9 components. +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 || 234 | 5 | 0.9791 + negative || 12 | 432 | 0.9730 + ||====================== +Precision || 0.9512 | 0.9886 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.084507 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.909522 +AUC: 0.996146 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996146 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.951220 (0.0000) +Positive recall: 0.979079 (0.0000) +Negative precision: 0.988558 (0.0000) +Negative recall: 0.972973 (0.0000) +Log-loss: 0.084507 (0.0000) +Log-loss reduction: 0.909522 (0.0000) +F1 Score: 0.964948 (0.0000) +AUPRC: 0.992065 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt new file mode 100644 index 0000000000..e852d92116 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.084507 0.909522 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali=PAV dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt new file mode 100644 index 0000000000..b45c3a1d1d --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.5726995 1E-15 1.4415419267167138E-15 0 +1 0 3.6101446 0.8333333 2.5849623287385155 1 +2 0 -4.0709443 1E-15 1.4415419267167138E-15 0 +3 0 2.470542 0.8095238 2.3923175087700885 1 +4 0 -3.4358397 1E-15 1.4415419267167138E-15 0 +5 1 12.382593 1 -0 1 +6 0 -1.4209604 0.071428575 0.10691520887754996 0 +7 0 -4.7010875 1E-15 1.4415419267167138E-15 0 +8 0 -4.6745405 1E-15 1.4415419267167138E-15 0 +9 0 -4.406417 1E-15 1.4415419267167138E-15 0 +10 0 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1.4415419267167138E-15 0 +35 0 -5.4818344 1E-15 1.4415419267167138E-15 0 +36 1 9.09911 1 -0 1 +37 0 -1.113348 0.071428575 0.10691520887754996 0 +38 1 6.140955 0.98 0.029146317580716615 1 +39 1 2.5109034 0.8095238 0.30485456129516797 1 +40 0 ? ? ? 0 +41 1 3.3300762 0.8333333 0.26303444023032446 1 +42 1 8.577511 1 -0 1 +43 1 0.49126053 0.6363636 0.65207672114864346 1 +44 1 8.255751 1 -0 1 +45 0 -5.63222 1E-15 1.4415419267167138E-15 0 +46 1 4.5673847 0.9285714 0.10691524360481655 1 +47 0 -5.95583 1E-15 1.4415419267167138E-15 0 +48 0 -3.4358397 1E-15 1.4415419267167138E-15 0 +49 1 5.3666534 0.98 0.029146317580716615 1 +50 1 2.5949688 0.8095238 0.30485456129516797 1 +51 1 0.12595844 0.6363636 0.65207672114864346 1 +52 1 5.2992115 0.98 0.029146317580716615 1 +53 1 8.407227 1 -0 1 +54 1 7.649309 1 -0 1 +55 1 4.478711 0.9285714 0.10691524360481655 1 +56 1 5.5541334 0.98 0.029146317580716615 1 +57 1 1.6657066 0.8095238 0.30485456129516797 1 +58 1 2.5265894 0.8095238 0.30485456129516797 1 +59 1 1.7368536 0.8095238 0.30485456129516797 1 +60 1 2.3288136 0.8095238 0.30485456129516797 1 +61 0 -5.5060835 1E-15 1.4415419267167138E-15 0 +62 1 6.380089 0.98 0.029146317580716615 1 +63 1 0.33490086 0.6363636 0.65207672114864346 1 +64 0 -5.95583 1E-15 1.4415419267167138E-15 0 +65 1 3.8072634 0.9285714 0.10691524360481655 1 +66 0 -4.046695 1E-15 1.4415419267167138E-15 0 +67 1 4.218014 0.9285714 0.10691524360481655 1 +68 1 10.826725 1 -0 1 +69 0 -5.271654 1E-15 1.4415419267167138E-15 0 +70 0 -3.4726496 1E-15 1.4415419267167138E-15 0 +71 1 7.895046 1 -0 1 +72 0 -2.1755848 0.071428575 0.10691520887754996 0 +73 1 8.9055195 1 -0 1 +74 1 2.5993576 0.8095238 0.30485456129516797 1 +75 0 -4.0411606 1E-15 1.4415419267167138E-15 0 +76 0 -5.0750337 1E-15 1.4415419267167138E-15 0 +77 0 -3.499567 1E-15 1.4415419267167138E-15 0 +78 0 -3.6211972 1E-15 1.4415419267167138E-15 0 +79 0 -5.391173 1E-15 1.4415419267167138E-15 0 +80 0 -2.7157316 1E-15 1.4415419267167138E-15 0 +81 0 -4.2284155 1E-15 1.4415419267167138E-15 0 +82 0 -3.4452734 1E-15 1.4415419267167138E-15 0 +83 0 -2.1223526 0.071428575 0.10691520887754996 0 +84 1 9.694054 1 -0 1 +85 1 6.2895603 0.98 0.029146317580716615 1 +86 1 2.6168842 0.8095238 0.30485456129516797 1 +87 1 6.919142 1 -0 1 +88 0 -4.046695 1E-15 1.4415419267167138E-15 0 +89 0 -5.085745 1E-15 1.4415419267167138E-15 0 +90 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +91 0 -5.189559 1E-15 1.4415419267167138E-15 0 +92 0 -4.046695 1E-15 1.4415419267167138E-15 0 +93 0 -5.95583 1E-15 1.4415419267167138E-15 0 +94 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +95 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +96 0 -5.663555 1E-15 1.4415419267167138E-15 0 +97 0 -3.5726995 1E-15 1.4415419267167138E-15 0 +98 1 8.590233 1 -0 1 +99 1 10.917194 1 -0 1 +100 1 4.8476696 0.9285714 0.10691524360481655 1 +101 1 -0.84280396 0.5 1 0 +102 0 -3.7530966 1E-15 1.4415419267167138E-15 0 +103 1 1.7746439 0.8095238 0.30485456129516797 1 +104 1 12.140858 1 -0 1 +105 1 2.5560713 0.8095238 0.30485456129516797 1 +106 1 9.259369 1 -0 1 +107 1 6.720646 0.98 0.029146317580716615 1 +108 0 -5.5617743 1E-15 1.4415419267167138E-15 0 +109 1 6.871727 1 -0 1 +110 0 -2.766693 1E-15 1.4415419267167138E-15 0 +111 1 3.848031 0.9285714 0.10691524360481655 1 +112 1 9.425768 1 -0 1 +113 1 9.506624 1 -0 1 +114 0 -3.0727458 1E-15 1.4415419267167138E-15 0 +115 0 -4.6439905 1E-15 1.4415419267167138E-15 0 +116 0 -0.66188717 0.5 1 0 +117 1 9.617275 1 -0 1 +118 0 -5.3621607 1E-15 1.4415419267167138E-15 0 +119 0 -3.9435177 1E-15 1.4415419267167138E-15 0 +120 0 -4.8696556 1E-15 1.4415419267167138E-15 0 +121 0 -3.469522 1E-15 1.4415419267167138E-15 0 +122 1 9.680523 1 -0 1 +123 1 3.8165932 0.9285714 0.10691524360481655 1 +124 1 7.6522446 1 -0 1 +125 0 -5.95583 1E-15 1.4415419267167138E-15 0 +126 1 8.564951 1 -0 1 +127 0 -4.520691 1E-15 1.4415419267167138E-15 0 +128 1 4.84898 0.9285714 0.10691524360481655 1 +129 0 -5.717684 1E-15 1.4415419267167138E-15 0 +130 0 -3.4726496 1E-15 1.4415419267167138E-15 0 +131 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +132 1 8.602232 1 -0 1 +133 0 -4.810811 1E-15 1.4415419267167138E-15 0 +134 0 -4.9171767 1E-15 1.4415419267167138E-15 0 +135 0 -2.7288966 1E-15 1.4415419267167138E-15 0 +136 0 -4.533843 1E-15 1.4415419267167138E-15 0 +137 0 -5.494987 1E-15 1.4415419267167138E-15 0 +138 0 -4.2402444 1E-15 1.4415419267167138E-15 0 +139 0 ? ? ? 0 +140 0 -5.494987 1E-15 1.4415419267167138E-15 0 +141 0 -5.9689827 1E-15 1.4415419267167138E-15 0 +142 1 4.4324036 0.9285714 0.10691524360481655 1 +143 0 -4.6439905 1E-15 1.4415419267167138E-15 0 +144 0 -5.4818344 1E-15 1.4415419267167138E-15 0 +145 0 ? ? ? 0 +146 1 1.3394346 0.8095238 0.30485456129516797 1 +147 0 -5.4154215 1E-15 1.4415419267167138E-15 0 +148 0 -1.0123739 0.071428575 0.10691520887754996 0 +149 1 11.461615 1 -0 1 +150 0 -5.5593443 1E-15 1.4415419267167138E-15 0 +151 1 5.006485 0.9285714 0.10691524360481655 1 +152 1 9.715748 1 -0 1 +153 0 -4.121497 1E-15 1.4415419267167138E-15 0 +154 0 -6.4429784 1E-15 1.4415419267167138E-15 0 +155 1 3.7769232 0.9285714 0.10691524360481655 1 +156 0 -5.5348053 1E-15 1.4415419267167138E-15 0 +157 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +158 0 ? ? ? 0 +159 1 12.346203 1 -0 1 +160 1 9.039494 1 -0 1 +161 0 -3.8496675 1E-15 1.4415419267167138E-15 0 +162 0 -4.520691 1E-15 1.4415419267167138E-15 0 +163 0 -3.3870554 1E-15 1.4415419267167138E-15 0 +164 0 ? ? ? 0 +165 0 -3.3999205 1E-15 1.4415419267167138E-15 0 +166 1 7.976185 1 -0 1 +167 1 8.355644 1 -0 1 +168 0 -4.520691 1E-15 1.4415419267167138E-15 0 +169 0 -6.2282124 1E-15 1.4415419267167138E-15 0 +170 0 -5.494987 1E-15 1.4415419267167138E-15 0 +171 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +172 0 -5.95583 1E-15 1.4415419267167138E-15 0 +173 1 15.1560135 1 -0 1 +174 1 6.1769257 0.98 0.029146317580716615 1 +175 1 7.842922 1 -0 1 +176 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +177 1 4.766121 0.9285714 0.10691524360481655 1 +178 0 -4.046695 1E-15 1.4415419267167138E-15 0 +179 1 2.290575 0.8095238 0.30485456129516797 1 +180 0 -5.5593443 1E-15 1.4415419267167138E-15 0 +181 0 -6.4429784 1E-15 1.4415419267167138E-15 0 +182 0 -3.0987039 1E-15 1.4415419267167138E-15 0 +183 1 9.1599655 1 -0 1 +184 1 6.2014637 0.98 0.029146317580716615 1 +185 0 -5.0853486 1E-15 1.4415419267167138E-15 0 +186 1 5.7654095 0.98 0.029146317580716615 1 +187 1 13.977451 1 -0 1 +188 1 9.065283 1 -0 1 +189 0 -4.7540584 1E-15 1.4415419267167138E-15 0 +190 1 11.957216 1 -0 1 +191 1 10.956871 1 -0 1 +192 0 -4.059848 1E-15 1.4415419267167138E-15 0 +193 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +194 0 -4.520691 1E-15 1.4415419267167138E-15 0 +195 0 -4.046695 1E-15 1.4415419267167138E-15 0 +196 0 6.8652763 0.98 5.6438575656365879 1 +197 0 -2.6564164 1E-15 1.4415419267167138E-15 0 +198 0 -6.4429784 1E-15 1.4415419267167138E-15 0 +199 0 -5.0078387 1E-15 1.4415419267167138E-15 0 +200 1 10.36586 1 -0 1 +201 1 9.869495 1 -0 1 +202 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +203 0 -3.5726995 1E-15 1.4415419267167138E-15 0 +204 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +205 1 12.086603 1 -0 1 +206 1 5.94417 0.98 0.029146317580716615 1 +207 0 -5.5593443 1E-15 1.4415419267167138E-15 0 +208 0 -5.5593443 1E-15 1.4415419267167138E-15 0 +209 0 -3.6633615 1E-15 1.4415419267167138E-15 0 +210 1 14.534115 1 -0 1 +211 1 9.64962 1 -0 1 +212 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +213 1 14.52906 1 -0 1 +214 1 13.868914 1 -0 1 +215 1 7.643734 1 -0 1 +216 0 -5.95583 1E-15 1.4415419267167138E-15 0 +217 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +218 1 7.88678 1 -0 1 +219 0 -2.511506 1E-15 1.4415419267167138E-15 0 +220 0 -5.1632547 1E-15 1.4415419267167138E-15 0 +221 1 10.395218 1 -0 1 +222 1 -2.214662 0.071428575 3.8073548575641118 0 +223 1 5.7424126 0.98 0.029146317580716615 1 +224 1 9.995327 1 -0 1 +225 0 -5.95583 1E-15 1.4415419267167138E-15 0 +226 1 10.225868 1 -0 1 +227 1 7.459608 1 -0 1 +228 0 -5.5593443 1E-15 1.4415419267167138E-15 0 +229 1 12.666517 1 -0 1 +230 1 6.1583214 0.98 0.029146317580716615 1 +231 1 8.623034 1 -0 1 +232 0 1.2822819 0.6363636 1.4594315756416352 1 +233 1 6.382519 0.98 0.029146317580716615 1 +234 0 -2.8964381 1E-15 1.4415419267167138E-15 0 +235 0 ? ? ? 0 +236 1 11.420414 1 -0 1 +237 1 6.5357933 0.98 0.029146317580716615 1 +238 1 12.422876 1 -0 1 +239 1 5.902529 0.98 0.029146317580716615 1 +240 0 -2.017991 0.071428575 0.10691520887754996 0 +241 0 -4.0004973 1E-15 1.4415419267167138E-15 0 +242 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +243 0 -2.6953988 1E-15 1.4415419267167138E-15 0 +244 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +245 0 -2.817525 1E-15 1.4415419267167138E-15 0 +246 1 11.424004 1 -0 1 +247 1 3.104393 0.8333333 0.26303444023032446 1 +248 0 -3.0615559 1E-15 1.4415419267167138E-15 0 +249 0 ? ? ? 0 +250 0 -6.021953 1E-15 1.4415419267167138E-15 0 +251 1 8.872498 1 -0 1 +252 0 4.5387735 0.9285714 3.8073544061097437 1 +253 1 8.577511 1 -0 1 +254 1 6.380089 0.98 0.029146317580716615 1 +255 1 4.052039 0.9285714 0.10691524360481655 1 +256 0 -5.494987 1E-15 1.4415419267167138E-15 0 +257 0 -5.0078387 1E-15 1.4415419267167138E-15 0 +258 0 -4.520691 1E-15 1.4415419267167138E-15 0 +259 0 2.9647493 0.8095238 2.3923175087700885 1 +260 1 9.870926 1 -0 1 +261 1 12.206299 1 -0 1 +262 1 9.653841 1 -0 1 +263 1 8.981979 1 -0 1 +264 1 5.664709 0.98 0.029146317580716615 1 +265 0 -2.494875 1E-15 1.4415419267167138E-15 0 +266 1 7.3661613 1 -0 1 +267 1 3.3009596 0.8333333 0.26303444023032446 1 +268 1 9.372967 1 -0 1 +269 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +270 1 6.031377 0.98 0.029146317580716615 1 +271 0 -3.5726995 1E-15 1.4415419267167138E-15 0 +272 1 3.3009596 0.8333333 0.26303444023032446 1 +273 1 0.21747208 0.6363636 0.65207672114864346 1 +274 0 -4.3236628 1E-15 1.4415419267167138E-15 0 +275 0 ? ? ? 0 +276 0 -5.0078387 1E-15 1.4415419267167138E-15 0 +277 0 -5.95583 1E-15 1.4415419267167138E-15 0 +278 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +279 1 7.127907 1 -0 1 +280 0 -4.520691 1E-15 1.4415419267167138E-15 0 +281 0 -4.6892586 1E-15 1.4415419267167138E-15 0 +282 1 4.4381247 0.9285714 0.10691524360481655 1 +283 1 6.0636253 0.98 0.029146317580716615 1 +284 1 7.431343 1 -0 1 +285 1 14.218479 1 -0 1 +286 1 15.281265 1 -0 1 +287 0 -4.9171767 1E-15 1.4415419267167138E-15 0 +288 1 2.2163515 0.8095238 0.30485456129516797 1 +289 1 8.312019 1 -0 1 +290 0 -6.4429784 1E-15 1.4415419267167138E-15 0 +291 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +292 1 ? ? ? 0 +293 1 5.542121 0.98 0.029146317580716615 1 +294 0 ? ? ? 0 +295 1 7.7866364 1 -0 1 +296 0 1.823431 0.8095238 2.3923175087700885 1 +297 0 ? ? ? 0 +298 0 -2.725597 1E-15 1.4415419267167138E-15 0 +299 1 7.8274345 1 -0 1 +300 1 7.348074 1 -0 1 +301 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +302 1 15.735764 1 -0 1 +303 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +304 1 5.9607983 0.98 0.029146317580716615 1 +305 1 8.459471 1 -0 1 +306 0 -5.4686823 1E-15 1.4415419267167138E-15 0 +307 0 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0.6363636 1.4594315756416352 1 +353 1 8.696342 1 -0 1 +354 0 -5.95583 1E-15 1.4415419267167138E-15 0 +355 0 -4.246153 1E-15 1.4415419267167138E-15 0 +356 1 -0.69921684 0.5 1 0 +357 1 12.852016 1 -0 1 +358 1 5.582206 0.98 0.029146317580716615 1 +359 1 5.3672857 0.98 0.029146317580716615 1 +360 1 15.333874 1 -0 1 +361 1 6.317689 0.98 0.029146317580716615 1 +362 0 -3.5059962 1E-15 1.4415419267167138E-15 0 +363 0 -2.0658464 0.071428575 0.10691520887754996 0 +364 0 -4.9946866 1E-15 1.4415419267167138E-15 0 +365 0 -5.4818344 1E-15 1.4415419267167138E-15 0 +366 1 13.694571 1 -0 1 +367 1 11.299244 1 -0 1 +368 0 -5.8557806 1E-15 1.4415419267167138E-15 0 +369 0 -5.4592943 1E-15 1.4415419267167138E-15 0 +370 0 -3.89473 1E-15 1.4415419267167138E-15 0 +371 0 -5.8557806 1E-15 1.4415419267167138E-15 0 +372 0 -4.2402444 1E-15 1.4415419267167138E-15 0 +373 0 -3.7544203 1E-15 1.4415419267167138E-15 0 +374 0 -4.71424 1E-15 1.4415419267167138E-15 0 +375 0 -6.4429784 1E-15 1.4415419267167138E-15 0 +376 0 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1.4415419267167138E-15 0 +690 0 -5.832123 1E-15 1.4415419267167138E-15 0 +691 1 4.4967804 0.9285714 0.10691524360481655 1 +692 0 -5.494987 1E-15 1.4415419267167138E-15 0 +693 0 -4.6842284 1E-15 1.4415419267167138E-15 0 +694 0 -4.9545784 1E-15 1.4415419267167138E-15 0 +695 0 -5.9689827 1E-15 1.4415419267167138E-15 0 +696 1 6.7127857 0.98 0.029146317580716615 1 +697 1 5.064644 0.98 0.029146317580716615 1 +698 1 6.1036224 0.98 0.029146317580716615 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt new file mode 100644 index 0000000000..d12e4da2b0 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) +Training calibrator. +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 || 234 | 5 | 0.9791 + negative || 12 | 432 | 0.9730 + ||====================== +Precision || 0.9512 | 0.9886 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.120617 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.870860 +AUC: 0.996146 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996146 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.951220 (0.0000) +Positive recall: 0.979079 (0.0000) +Negative precision: 0.988558 (0.0000) +Negative recall: 0.972973 (0.0000) +Log-loss: 0.120617 (0.0000) +Log-loss reduction: 0.870860 (0.0000) +F1 Score: 0.964948 (0.0000) +AUPRC: 0.992065 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt new file mode 100644 index 0000000000..0860eb7030 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 0.120617 0.87086 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron numcali=200 dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt new file mode 100644 index 0000000000..48a92460de --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.5726995 0.047724232 0.070548673242470078 0 +1 0 3.6101446 0.92095214 3.6611298070520353 1 +2 0 -4.0709443 0.03320216 0.048713845243635014 0 +3 0 2.470542 0.83073896 2.5626781731328263 1 +4 0 -3.4358397 0.05267027 0.078061433982946932 0 +5 1 12.382593 0.99988943 0.0001595227277818751 1 +6 0 -1.4209604 0.20405398 0.32925750806385767 0 +7 0 -4.7010875 0.02084881 0.030396453391412736 0 +8 0 -4.6745405 0.021263903 0.031008185910590607 0 +9 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0.97231287 0.040507480679364792 1 +698 1 6.1036224 0.9872181 0.018559275661035816 1 diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt new file mode 100644 index 0000000000..5d98ed80f7 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt @@ -0,0 +1,39 @@ +maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) +Not training a calibrator because a valid calibrator trainer was not provided. +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 || 234 | 5 | 0.9791 + negative || 12 | 432 | 0.9730 + ||====================== +Precision || 0.9512 | 0.9886 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: NaN +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.000000 +AUC: 0.996146 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996146 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.951220 (0.0000) +Positive recall: 0.979079 (0.0000) +Negative precision: 0.988558 (0.0000) +Negative recall: 0.972973 (0.0000) +Log-loss: NaN (0.0000) +Log-loss reduction: 0.000000 (0.0000) +F1 Score: 0.964948 (0.0000) +AUPRC: 0.992065 (0.0000) + +--------------------------------------- +Warning: Data does not contain a probability column. Will not output the Log-loss column +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt new file mode 100644 index 0000000000..ac4bbeaf12 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt @@ -0,0 +1,4 @@ +AveragedPerceptron +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996146 0.97511 0.95122 0.979079 0.988558 0.972973 NaN 0 0.964948 0.992065 AveragedPerceptron %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=AveragedPerceptron cali={} dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt new file mode 100644 index 0000000000..9bb514daf9 --- /dev/null +++ b/test/BaselineOutput/Common/AveragedPerceptron/win-arm/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt @@ -0,0 +1,700 @@ +Instance Label Score Assigned +0 0 -3.5726995 0 +1 0 3.6101446 1 +2 0 -4.0709443 0 +3 0 2.470542 1 +4 0 -3.4358397 0 +5 1 12.382593 1 +6 0 -1.4209604 0 +7 0 -4.7010875 0 +8 0 -4.6745405 0 +9 0 -4.406417 0 +10 0 -5.5593443 0 +11 0 -5.4818344 0 +12 1 -0.14206886 0 +13 0 -4.5691886 0 +14 1 9.321613 1 +15 1 1.3856993 1 +16 0 -4.533843 0 +17 0 -4.046695 0 +18 1 7.8903713 1 +19 0 -3.0987039 0 +20 1 7.528511 1 +21 1 7.875206 1 +22 0 -5.0078387 0 +23 1 ? 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-1.8253045 0.009010682 0.013058588214213173 0 +694 0 -1.7690041 0.010231109 0.014836397186509614 0 +695 0 -2.1563811 0.004258803 0.0061572747374996819 0 +696 1 2.527448 0.9945974 0.00781547081749355 1 +697 1 1.4814918 0.9444188 0.082501352535462646 1 +698 1 1.8482563 0.9751135 0.036357924898776386 1 diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-1-out.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-1-out.txt new file mode 100644 index 0000000000..09df0d1081 --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-1-out.txt @@ -0,0 +1 @@ +Saving predictor summary diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-out.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-out.txt new file mode 100644 index 0000000000..ea653a25ad --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-out.txt @@ -0,0 +1,15 @@ +maml.exe Train tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,} data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Model trained with 150 training examples. +Residual Deviance: 132.03748 +Null Deviance: 329.58368 +AIC: 154.03748 +Not training a calibrator because it is not needed. +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt new file mode 100644 index 0000000000..a62d44920c --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainMlrWithStats-summary.txt @@ -0,0 +1,18 @@ +LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights + Iris-setosa+(Bias) 2.2919443 + Iris-versicolor+(Bias) 0.76702803 + Iris-virginica+(Bias) -3.059023 + Iris-setosa+f3 -3.1749554 + Iris-setosa+f2 -2.887113 + Iris-setosa+f1 0.5143879 + Iris-setosa+f0 -0.058297563 + Iris-versicolor+f1 -0.70855236 + Iris-virginica+f3 3.1559532 + Iris-virginica+f2 1.9136102 + Iris-virginica+f0 0.033977978 + +*** MODEL STATISTICS SUMMARY *** +Count of training examples: 150 +Residual Deviance: 132.03748 +Null Deviance: 329.58368 +AIC: 154.03748 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..4fb66c0381 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +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 || 124 | 10 | 0.9254 + negative || 5 | 215 | 0.9773 + ||====================== +Precision || 0.9612 | 0.9556 | +OVERALL 0/1 ACCURACY: 0.957627 +LOG LOSS/instance: 0.166325 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.826202 +AUC: 0.994199 +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 || 100 | 5 | 0.9524 + negative || 4 | 220 | 0.9821 + ||====================== +Precision || 0.9615 | 0.9778 | +OVERALL 0/1 ACCURACY: 0.972644 +LOG LOSS/instance: 0.182604 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.797882 +AUC: 0.961054 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.977627 (0.0166) +Accuracy: 0.965136 (0.0075) +Positive precision: 0.961389 (0.0001) +Positive recall: 0.938877 (0.0135) +Negative precision: 0.966667 (0.0111) +Negative recall: 0.979708 (0.0024) +Log-loss: 0.174464 (0.0081) +Log-loss reduction: 0.812042 (0.0142) +F1 Score: 0.949952 (0.0070) +AUPRC: 0.979909 (0.0095) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..d540f25184 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LdSvm +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.977627 0.965136 0.961389 0.938877 0.966667 0.979708 0.174464 0.812042 0.949952 0.979909 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt new file mode 100644 index 0000000000..5acbf41e17 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.526149 0.9999277 0.00010431124866801515 1 +6 0 -1.1218108 0.0914504 0.13836281897248148 0 +8 0 -1.6203799 0.027515754 0.040253216049532151 0 +9 0 -1.669663 0.024350794 0.035565573924712414 0 +10 0 -1.8151165 0.016943678 0.02465402036879874 0 +11 0 -1.8067945 0.017300142 0.02517724745610924 0 +18 1 2.0538619 0.9969428 0.0044173368194155311 1 +20 1 1.8093374 0.99431825 0.0082204124602030648 1 +21 1 2.1534398 0.9976257 0.0034294513286414175 1 +25 1 -0.6382538 0.25631282 1.9640224701655145 0 +28 0 -1.8067945 0.017300142 0.02517724745610924 0 +31 0 -1.728947 0.021011736 0.030636530178365998 0 +32 1 2.5068038 0.9990328 0.0013960564827661087 1 +35 0 -1.8067945 0.017300142 0.02517724745610924 0 +37 0 -1.1458213 0.08649767 0.13051968926295085 0 +40 0 ? ? ? 0 +41 1 -0.20914128 0.50675654 0.98063528051212701 0 +44 1 2.8598988 0.999606 0.00056851466206539339 1 +45 0 -1.7839876 0.018315233 0.026668266391998201 0 +46 1 1.2672604 0.9777957 0.0323950043331281 1 +48 0 -1.5373471 0.033772714 0.049565500871283592 0 +50 1 0.9638393 0.95314443 0.069233250884230338 1 +51 1 -0.85461456 0.1657656 2.5927834616592653 0 +52 1 1.3377451 0.98137456 0.027124219099611876 1 +54 1 2.3109157 0.99840856 0.0022977972822397406 1 +56 1 1.6193739 0.9908179 0.013308155996179006 1 +60 1 -0.41534865 0.37804702 1.4033624154398172 0 +63 1 -0.7428849 0.20890367 2.2590902549815062 0 +64 0 -1.8474231 0.015627015 0.022723030236888715 0 +66 0 -1.6305369 0.026832342 0.039239720183993521 0 +68 1 2.9473996 0.9996847 0.00045496584261690929 1 +69 0 -1.7663403 0.019140588 0.027881727370439507 0 +70 0 -1.4818044 0.03870289 0.05694569877155152 0 +71 1 2.8194919 0.9995634 0.00063002400430274086 1 +72 0 -1.0894251 0.09853413 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0.012108513451261255 0 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..4aa31718a3 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 16 rows with missing feature/label values +Training calibrator. +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 || 232 | 7 | 0.9707 + negative || 11 | 433 | 0.9752 + ||====================== +Precision || 0.9547 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.111359 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.880772 +AUC: 0.996136 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996136 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.954733 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984091 (0.0000) +Negative recall: 0.975225 (0.0000) +Log-loss: 0.111359 (0.0000) +Log-loss reduction: 0.880772 (0.0000) +F1 Score: 0.962656 (0.0000) +AUPRC: 0.992151 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..b6a73dfb8d --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LdSvm +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996136 0.973646 0.954733 0.970711 0.984091 0.975225 0.111359 0.880772 0.962656 0.992151 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..18ed793356 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-def-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1.6978589 0.014107211 0.020497325114984831 0 +1 0 1.2372239 0.90693176 3.425567232731229 1 +2 0 -1.8189844 0.0108135985 0.015685688008012112 0 +3 0 1.1770016 0.89500177 3.2515630740820534 1 +4 0 -1.7234358 0.0133380145 0.019372169618365857 0 +5 1 4.017951 0.9997877 0.00030633362145029628 1 +6 0 -0.97414255 0.06671321 0.099607613998291863 0 +7 0 -1.9396354 0.008291152 0.012011467865383887 0 +8 0 -1.8314458 0.01052131 0.015259458572543063 0 +9 0 -1.825638 0.010656549 0.015456654263451611 0 +10 0 -2.0861132 0.006001023 0.0086837280459640571 0 +11 0 -2.0513897 0.0064793876 0.0093781958119407548 0 +12 1 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0.96654 0.049098687039799346 1 +692 0 -2.0231707 0.0068959 0.0099831414740292716 0 +693 0 -1.9052485 0.008943793 0.01296121433157176 0 +694 0 -1.9114863 0.008821737 0.012783546065593747 0 +695 0 -2.0886197 0.005967883 0.0086356290226746457 0 +696 1 2.564204 0.99465436 0.0077328189280636388 1 +697 1 1.6150802 0.957571 0.062548589215837253 1 +698 1 1.9523501 0.9794915 0.029895162889108172 1 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..3511d8cc47 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-out.txt @@ -0,0 +1,56 @@ +maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 rows with missing feature/label values +Training calibrator. +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 || 121 | 13 | 0.9030 + negative || 14 | 206 | 0.9364 + ||====================== +Precision || 0.8963 | 0.9406 | +OVERALL 0/1 ACCURACY: 0.923729 +LOG LOSS/instance: 0.332232 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.652840 +AUC: 0.969878 +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 || 102 | 3 | 0.9714 + negative || 54 | 170 | 0.7589 + ||====================== +Precision || 0.6538 | 0.9827 | +OVERALL 0/1 ACCURACY: 0.826748 +LOG LOSS/instance: 0.402185 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.554836 +AUC: 0.968878 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.969378 (0.0005) +Accuracy: 0.875238 (0.0485) +Positive precision: 0.775071 (0.1212) +Positive recall: 0.937207 (0.0342) +Negative precision: 0.961649 (0.0210) +Negative recall: 0.847646 (0.0887) +Log-loss: 0.367208 (0.0350) +Log-loss reduction: 0.603838 (0.0490) +F1 Score: 0.840619 (0.0590) +AUPRC: 0.962365 (0.0123) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..55c052227f --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LdSvm +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.969378 0.875238 0.775071 0.937207 0.961649 0.847646 0.367208 0.603838 0.840619 0.962365 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt new file mode 100644 index 0000000000..5638d5909d --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.8534284 0.9966877 0.00478655555839757 1 +6 0 1.9441669 0.9089837 3.4577313685410616 1 +8 0 -1.3064579 0.029403893 0.04305702174461324 0 +9 0 -0.92505693 0.056439757 0.083813463651964668 0 +10 0 -0.75773776 0.07460297 0.11185562463682144 0 +11 0 -0.9906934 0.050519254 0.074789349927547044 0 +18 1 2.413366 0.95844084 0.061238710589369388 1 +20 1 1.568261 0.83627826 0.25794503634600341 1 +21 1 1.7287251 0.8718046 0.19792328720715538 1 +25 1 2.2633896 0.9463779 0.079511751428780225 1 +28 0 -0.9906934 0.050519254 0.074789349927547044 0 +31 0 -1.2457733 0.032655496 0.04789832154160352 0 +32 1 -0.24883446 0.16654387 2.5860258312595557 0 +35 0 -0.9906934 0.050519254 0.074789349927547044 0 +37 0 -2.2412913 0.0056849807 0.0082250953708129255 0 +40 0 ? ? ? 0 +41 1 -0.36874965 0.13892916 2.8475786709803779 0 +44 1 1.0931432 0.68639857 0.54288155656014681 1 +45 0 -0.97998923 0.051442977 0.076193590341433137 0 +46 1 4.6290503 0.9991675 0.001201541052029201 1 +48 0 -1.5960495 0.017752413 0.025841375908303817 0 +50 1 -0.026029551 0.2291866 2.1254054340117103 0 +51 1 1.164235 0.7130289 0.48796752697067669 1 +52 1 1.5556282 0.8331697 0.26331772347354976 1 +54 1 -0.39825928 0.1327513 2.9132020957225473 0 +56 1 4.295429 0.9984916 0.0021778254179274439 1 +60 1 1.99921 0.91678673 0.12534193242882088 1 +63 1 0.5764582 0.46548873 1.1031818501641952 1 +64 0 -0.8386307 0.065233134 0.097321497608580276 0 +66 0 -1.5475866 0.019324278 0.028151933228968792 0 +68 1 1.4756343 0.8123842 0.29976593484859421 1 +69 0 -0.8957717 0.059286844 0.088173214102917494 0 +70 0 -1.3289838 0.028278641 0.041385415007353897 0 +71 1 -0.122156024 0.2003083 2.3197059424707156 0 +72 0 -1.1036713 0.041683324 0.061425620700316604 0 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0.011004884765518618 0 +687 0 -0.1104493 0.015770128 0.022932791041876899 0 +690 0 -0.2828443 0.010268201 0.014890463480481621 0 +695 0 -0.36452106 0.008373251 0.012130906699393275 0 diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..637b1e19ce --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-out.txt @@ -0,0 +1,38 @@ +maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 16 rows with missing feature/label values +Training calibrator. +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 || 220 | 19 | 0.9205 + negative || 25 | 419 | 0.9437 + ||====================== +Precision || 0.8980 | 0.9566 | +OVERALL 0/1 ACCURACY: 0.935578 +LOG LOSS/instance: 0.315898 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.661780 +AUC: 0.963945 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.963945 (0.0000) +Accuracy: 0.935578 (0.0000) +Positive precision: 0.897959 (0.0000) +Positive recall: 0.920502 (0.0000) +Negative precision: 0.956621 (0.0000) +Negative recall: 0.943694 (0.0000) +Log-loss: 0.315898 (0.0000) +Log-loss reduction: 0.661780 (0.0000) +F1 Score: 0.909091 (0.0000) +AUPRC: 0.951812 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..8f8e4e2508 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LdSvm +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.963945 0.935578 0.897959 0.920502 0.956621 0.943694 0.315898 0.66178 0.909091 0.951812 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000 + diff --git a/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..50ffce0106 --- /dev/null +++ b/test/BaselineOutput/Common/LdSvm/win-arm/LDSVM-nob-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1.9210272 0.009717667 0.014088192920503215 0 +1 0 1.5968752 0.8927986 3.2216043827830356 1 +2 0 -1.2856477 0.032107096 0.047080670997454895 0 +3 0 2.7900536 0.9879544 6.3753473131417593 1 +4 0 -1.6028564 0.017738463 0.025820887678307834 0 +5 1 3.5799701 0.9973251 0.0038642031538066459 1 +6 0 1.7048188 0.91105425 3.4909305754333388 1 +7 0 -1.3287921 0.029634012 0.043399110868457463 0 +8 0 -1.5092304 0.021151947 0.030843167633737175 0 +9 0 -1.1032926 0.044938717 0.066334786043110966 0 +10 0 -0.80262655 0.0772644 0.11601077400748538 0 +11 0 -1.1343119 0.042454563 0.062587148254938621 0 +12 1 0.52196103 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a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..49cb5e526d --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-out.txt @@ -0,0 +1,112 @@ +maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 130 | 4 | 0.9701 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9489 | 0.9816 | +OVERALL 0/1 ACCURACY: 0.968927 +LOG LOSS/instance: 0.143504 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.850048 +AUC: 0.994132 +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 || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9697 | 0.9609 | +OVERALL 0/1 ACCURACY: 0.963526 +LOG LOSS/instance: 0.111794 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.876259 +AUC: 0.997236 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995684 (0.0016) +Accuracy: 0.966226 (0.0027) +Positive precision: 0.959301 (0.0104) +Positive recall: 0.942217 (0.0279) +Negative precision: 0.971218 (0.0103) +Negative recall: 0.977394 (0.0092) +Log-loss: 0.127649 (0.0159) +Log-loss reduction: 0.863153 (0.0131) +F1 Score: 0.950293 (0.0091) +AUPRC: 0.991584 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387 +[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725 +[2] (%Time%) 3 iterations Loss: 0.5179428458213806 Improvement: 0.09906 +[2] (%Time%) 4 iterations Loss: 0.4057178199291229 Improvement: 0.109 +[2] (%Time%) 5 iterations Loss: 0.3352813422679901 Improvement: 0.08004 +[2] (%Time%) 6 iterations Loss: 0.3197685182094574 Improvement: 0.03163 +[2] (%Time%) 7 iterations Loss: 0.31049972772598267 Improvement: 0.01486 +[2] (%Time%) 8 iterations Loss: 0.2985224425792694 Improvement: 0.0127 +[2] (%Time%) 9 iterations Loss: 0.2738857865333557 Improvement: 0.02165 +[2] (%Time%) 10 iterations Loss: 0.2316397875547409 Improvement: 0.0371 +[2] (%Time%) 11 iterations Loss: 0.17319267988204956 Improvement: 0.05311 +[2] (%Time%) 12 iterations Loss: 0.13885831832885742 Improvement: 0.03903 +[2] (%Time%) 13 iterations Loss: 0.11185470223426819 Improvement: 0.03001 +[2] (%Time%) 14 iterations Loss: 0.10666187852621078 Improvement: 0.0114 +[2] (%Time%) 15 iterations Loss: 0.09659970551729202 Improvement: 0.0104 +[2] (%Time%) 16 iterations Loss: 0.08454433083534241 Improvement: 0.01164 +[2] (%Time%) 17 iterations Loss: 0.08044131845235825 Improvement: 0.005987 +[2] (%Time%) 18 iterations Loss: 0.07466444373130798 Improvement: 0.00583 +[2] (%Time%) 19 iterations Loss: 0.07378975301980972 Improvement: 0.002113 +[2] (%Time%) 20 iterations Loss: 0.07328920811414719 Improvement: 0.0009038 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'LBFGS data prep #2' started. +[3] 'LBFGS data prep #2' finished in %Time%. +[4] 'LBFGS Optimizer #2' started. +[4] (%Time%) 0 iterations Loss: 0.6931471824645996 +[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603 +[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156 +[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083 +[4] (%Time%) 4 iterations Loss: 0.40101736783981323 Improvement: 0.1627 +[4] (%Time%) 5 iterations Loss: 0.36694765090942383 Improvement: 0.06612 +[4] (%Time%) 6 iterations Loss: 0.3179488480091095 Improvement: 0.05328 +[4] (%Time%) 7 iterations Loss: 0.2625056505203247 Improvement: 0.0549 +[4] (%Time%) 8 iterations Loss: 0.21261096000671387 Improvement: 0.05115 +[4] (%Time%) 9 iterations Loss: 0.17287731170654297 Improvement: 0.04259 +[4] (%Time%) 10 iterations Loss: 0.14646978676319122 Improvement: 0.03045 +[4] (%Time%) 11 iterations Loss: 0.11033976078033447 Improvement: 0.03471 +[4] (%Time%) 12 iterations Loss: 0.10183008015155792 Improvement: 0.01506 +[4] (%Time%) 13 iterations Loss: 0.09570615738630295 Improvement: 0.008358 +[4] (%Time%) 14 iterations Loss: 0.09520583599805832 Improvement: 0.002465 +[4] (%Time%) 15 iterations Loss: 0.08928800374269485 Improvement: 0.005055 +[4] (%Time%) 16 iterations Loss: 0.08804423362016678 Improvement: 0.002196 +[4] (%Time%) 17 iterations Loss: 0.0877317488193512 Improvement: 0.0007835 +[4] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..e2cfdef839 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995684 0.966226 0.959301 0.942217 0.971218 0.977394 0.127649 0.863153 0.950293 0.991584 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.txt new file mode 100644 index 0000000000..4d6066637e --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 11.843378 0.9999928 1.0404987898474284E-05 1 +6 0 -0.59487915 0.35551614 0.63378385604457876 0 +8 0 -4.651406 0.009457864 0.013709748448272217 0 +9 0 -4.4850006 0.01115113 0.016178050759675634 0 +10 0 -5.430295 0.0043626865 0.0063077957265197963 0 +11 0 -5.546282 0.0038867726 0.005618353203892556 0 +18 1 7.386753 0.999381 0.00089329641580267009 1 +20 1 8.050364 0.9996811 0.00046012695871573505 1 +21 1 7.1970863 0.9992518 0.001079853140832915 1 +25 1 1.1823997 0.765379 0.38575375519663319 1 +28 0 -5.546282 0.0038867726 0.005618353203892556 0 +31 0 -4.911649 0.007306561 0.010579837492727742 0 +32 1 7.011631 0.9990995 0.0012997422459666663 1 +35 0 -5.546282 0.0038867726 0.005618353203892556 0 +37 0 -0.62716675 0.34815323 0.61739523354932679 0 +40 0 ? ? ? 0 +41 1 3.0365572 0.9541986 0.067638527006529184 1 +44 1 6.6134357 0.9986596 0.0019350700897898159 1 +45 0 -5.56501 0.0038149324 0.0055143091492393511 0 +46 1 3.9145546 0.98044074 0.028497667659147116 1 +48 0 -3.536106 0.028302178 0.041420359423840737 0 +50 1 1.0294714 0.7368134 0.44062874481429237 1 +51 1 -0.36414146 0.4099574 1.2864540611401696 0 +52 1 3.4916897 0.9704504 0.043273614682259293 1 +54 1 7.767933 0.9995771 0.00061023749621146994 1 +56 1 3.6949062 0.9757528 0.035412440314029049 1 +60 1 0.8933983 0.709591 0.49494044136238835 1 +63 1 -0.91438866 0.28610262 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b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold-out.txt @@ -0,0 +1,112 @@ +maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 118 | 16 | 0.8806 + negative || 3 | 217 | 0.9864 + ||====================== +Precision || 0.9752 | 0.9313 | +OVERALL 0/1 ACCURACY: 0.946328 +LOG LOSS/instance: 0.143504 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.850048 +AUC: 0.994132 +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 || 81 | 24 | 0.7714 + negative || 0 | 224 | 1.0000 + ||====================== +Precision || 1.0000 | 0.9032 | +OVERALL 0/1 ACCURACY: 0.927052 +LOG LOSS/instance: 0.111794 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.876259 +AUC: 0.997236 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995684 (0.0016) +Accuracy: 0.936690 (0.0096) +Positive precision: 0.987603 (0.0124) +Positive recall: 0.826013 (0.0546) +Negative precision: 0.917278 (0.0141) +Negative recall: 0.993182 (0.0068) +Log-loss: 0.127649 (0.0159) +Log-loss reduction: 0.863153 (0.0131) +F1 Score: 0.898229 (0.0273) +AUPRC: 0.991584 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387 +[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725 +[2] (%Time%) 3 iterations Loss: 0.5179428458213806 Improvement: 0.09906 +[2] (%Time%) 4 iterations Loss: 0.4057178199291229 Improvement: 0.109 +[2] (%Time%) 5 iterations Loss: 0.3352813422679901 Improvement: 0.08004 +[2] (%Time%) 6 iterations Loss: 0.3197685182094574 Improvement: 0.03163 +[2] (%Time%) 7 iterations Loss: 0.31049972772598267 Improvement: 0.01486 +[2] (%Time%) 8 iterations Loss: 0.2985224425792694 Improvement: 0.0127 +[2] (%Time%) 9 iterations Loss: 0.2738857865333557 Improvement: 0.02165 +[2] (%Time%) 10 iterations Loss: 0.2316397875547409 Improvement: 0.0371 +[2] (%Time%) 11 iterations Loss: 0.17319267988204956 Improvement: 0.05311 +[2] (%Time%) 12 iterations Loss: 0.13885831832885742 Improvement: 0.03903 +[2] (%Time%) 13 iterations Loss: 0.11185470223426819 Improvement: 0.03001 +[2] (%Time%) 14 iterations Loss: 0.10666187852621078 Improvement: 0.0114 +[2] (%Time%) 15 iterations Loss: 0.09659970551729202 Improvement: 0.0104 +[2] (%Time%) 16 iterations Loss: 0.08454433083534241 Improvement: 0.01164 +[2] (%Time%) 17 iterations Loss: 0.08044131845235825 Improvement: 0.005987 +[2] (%Time%) 18 iterations Loss: 0.07466444373130798 Improvement: 0.00583 +[2] (%Time%) 19 iterations Loss: 0.07378975301980972 Improvement: 0.002113 +[2] (%Time%) 20 iterations Loss: 0.07328920811414719 Improvement: 0.0009038 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'LBFGS data prep #2' started. +[3] 'LBFGS data prep #2' finished in %Time%. +[4] 'LBFGS Optimizer #2' started. +[4] (%Time%) 0 iterations Loss: 0.6931471824645996 +[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603 +[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156 +[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083 +[4] (%Time%) 4 iterations Loss: 0.40101736783981323 Improvement: 0.1627 +[4] (%Time%) 5 iterations Loss: 0.36694765090942383 Improvement: 0.06612 +[4] (%Time%) 6 iterations Loss: 0.3179488480091095 Improvement: 0.05328 +[4] (%Time%) 7 iterations Loss: 0.2625056505203247 Improvement: 0.0549 +[4] (%Time%) 8 iterations Loss: 0.21261096000671387 Improvement: 0.05115 +[4] (%Time%) 9 iterations Loss: 0.17287731170654297 Improvement: 0.04259 +[4] (%Time%) 10 iterations Loss: 0.14646978676319122 Improvement: 0.03045 +[4] (%Time%) 11 iterations Loss: 0.11033976078033447 Improvement: 0.03471 +[4] (%Time%) 12 iterations Loss: 0.10183008015155792 Improvement: 0.01506 +[4] (%Time%) 13 iterations Loss: 0.09570615738630295 Improvement: 0.008358 +[4] (%Time%) 14 iterations Loss: 0.09520583599805832 Improvement: 0.002465 +[4] (%Time%) 15 iterations Loss: 0.08928800374269485 Improvement: 0.005055 +[4] (%Time%) 16 iterations Loss: 0.08804423362016678 Improvement: 0.002196 +[4] (%Time%) 17 iterations Loss: 0.0877317488193512 Improvement: 0.0007835 +[4] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt new file mode 100644 index 0000000000..e0c26e24d7 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995684 0.93669 0.987603 0.826013 0.917278 0.993182 0.127649 0.863153 0.898229 0.991584 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold.txt new file mode 100644 index 0000000000..766e52b7f9 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-CV-breast-cancer.withThreshold.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 11.843378 0.9999928 1.0404987898474284E-05 1 +6 0 -0.59487915 0.35551614 0.63378385604457876 0 +8 0 -4.651406 0.009457864 0.013709748448272217 0 +9 0 -4.4850006 0.01115113 0.016178050759675634 0 +10 0 -5.430295 0.0043626865 0.0063077957265197963 0 +11 0 -5.546282 0.0038867726 0.005618353203892556 0 +18 1 7.386753 0.999381 0.00089329641580267009 1 +20 1 8.050364 0.9996811 0.00046012695871573505 1 +21 1 7.1970863 0.9992518 0.001079853140832915 1 +25 1 1.1823997 0.765379 0.38575375519663319 0 +28 0 -5.546282 0.0038867726 0.005618353203892556 0 +31 0 -4.911649 0.007306561 0.010579837492727742 0 +32 1 7.011631 0.9990995 0.0012997422459666663 1 +35 0 -5.546282 0.0038867726 0.005618353203892556 0 +37 0 -0.62716675 0.34815323 0.61739523354932679 0 +40 0 ? ? ? 0 +41 1 3.0365572 0.9541986 0.067638527006529184 1 +44 1 6.6134357 0.9986596 0.0019350700897898159 1 +45 0 -5.56501 0.0038149324 0.0055143091492393511 0 +46 1 3.9145546 0.98044074 0.028497667659147116 1 +48 0 -3.536106 0.028302178 0.041420359423840737 0 +50 1 1.0294714 0.7368134 0.44062874481429237 0 +51 1 -0.36414146 0.4099574 1.2864540611401696 0 +52 1 3.4916897 0.9704504 0.043273614682259293 1 +54 1 7.767933 0.9995771 0.00061023749621146994 1 +56 1 3.6949062 0.9757528 0.035412440314029049 1 +60 1 0.8933983 0.709591 0.49494044136238835 0 +63 1 -0.91438866 0.28610262 1.8053953717549116 0 +64 0 -5.947387 0.0026058496 0.0037643531610362002 0 +66 0 -4.1094394 0.016151812 0.023492376082898361 0 +68 1 9.950766 0.9999523 6.8794700551634382E-05 1 +69 0 -5.1962605 0.0055067404 0.0079665020079430701 0 +70 0 -4.2990217 0.013399845 0.019462580884246464 0 +71 1 9.312965 0.99990976 0.00013019674136170853 1 +72 0 -1.8089752 0.14076203 0.21887034881184167 0 +73 1 7.979315 0.99965763 0.00049401874647617746 1 +74 1 2.2298784 0.9029007 0.14736077113903101 0 +76 0 -5.8814692 0.0027829157 0.0040204956554648792 0 +77 0 -3.5381575 0.028245818 0.041336683395315918 0 +79 0 -5.4287415 0.00436944 0.0063175816728364229 0 +82 0 -3.449263 0.030790843 0.045120059685660907 0 +88 0 -4.1094394 0.016151812 0.023492376082898361 0 +90 0 -5.312754 0.0049041654 0.0070926210434774928 0 +91 0 -5.493143 0.0040980275 0.0059243513065432981 0 +92 0 -4.1094394 0.016151812 0.023492376082898361 0 +93 0 -5.947387 0.0026058496 0.0037643531610362002 0 +95 0 -5.312754 0.0049041654 0.0070926210434774928 0 +96 0 -5.894248 0.002747677 0.0039695160053964361 0 +97 0 -3.708334 0.023931576 0.034945808171670999 0 +98 1 6.953968 0.9990461 0.0013768619797486616 1 +99 1 10.508131 0.9999727 3.9384564628212499E-05 1 +100 1 3.3712034 0.96679235 0.048722044835642818 1 +102 0 -4.0874667 0.016504716 0.024009960483151255 0 +104 1 11.012169 0.9999835 2.3819793783233171E-05 1 +105 1 2.1146507 0.892319 0.16436849741706283 0 +106 1 8.185759 0.99972147 0.00040189343653238463 1 +108 0 -5.7144 0.0032872874 0.0047503654649493484 0 +109 1 7.084921 0.99916303 0.0012079957894204731 1 +111 1 3.9758768 0.9815827 0.026818270809047463 1 +112 1 9.693426 0.9999383 8.900376720685931E-05 1 +113 1 9.182865 0.99989724 0.00014825666248348076 1 +115 0 -4.4851866 0.0111490805 0.016175060118282753 0 +117 1 10.228838 0.9999639 5.2111684364922767E-05 1 +120 0 -5.004115 0.006665549 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0.0034485627 0.0049838227609212849 0 +161 0 -3.9929457 0.018111233 0.026368495915666784 0 +164 0 ? ? ? 0 +167 1 6.799999 0.9988875 0.0016059215653949079 1 +169 0 -6.412894 0.001637585 0.0023644722930159967 0 +171 0 -5.312754 0.0049041654 0.0070926210434774928 0 +173 1 15.010418 0.9999997 4.2995669122556443E-07 1 +174 1 6.477541 0.99846476 0.0022165804928464235 1 +176 0 -4.911649 0.007306561 0.010579837492727742 0 +177 1 3.1821003 0.96015507 0.058660667798257342 1 +179 1 2.6151247 0.9318287 0.10186336384757579 0 +180 0 -5.430295 0.0043626865 0.0063077957265197963 0 +181 0 -6.58202 0.0013831332 0.0019968207086486669 0 +183 1 8.690121 0.9998318 0.00024268793151739886 1 +187 1 13.8902645 0.99999905 1.3758618629646341E-06 1 +188 1 9.6294 0.99993426 9.4851549941511306E-05 1 +189 0 -5.009738 0.0066284225 0.0095946261803761847 0 +191 1 9.013945 0.9998783 0.00017560497342409987 1 +192 0 -4.3429666 0.012831134 0.018631200068976669 0 +196 0 6.601473 0.99864346 9.5258504515026932 1 +198 0 -6.58202 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0000000000..a4388f29e5 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt @@ -0,0 +1,107 @@ +maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=MeanVarNormalizer{col=Features} +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 9 of 10 weights. +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 || 129 | 5 | 0.9627 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9485 | 0.9771 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.133256 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.860756 +AUC: 0.994267 +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 || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9697 | 0.9609 | +OVERALL 0/1 ACCURACY: 0.963526 +LOG LOSS/instance: 0.117262 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.870207 +AUC: 0.997449 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995858 (0.0016) +Accuracy: 0.964814 (0.0013) +Positive precision: 0.959113 (0.0106) +Positive recall: 0.938486 (0.0242) +Negative precision: 0.968967 (0.0081) +Negative recall: 0.977394 (0.0092) +Log-loss: 0.125259 (0.0080) +Log-loss reduction: 0.865481 (0.0047) +F1 Score: 0.948366 (0.0072) +AUPRC: 0.991982 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 337 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.6098362803459167 Improvement: 0.08331 +[3] (%Time%) 2 iterations Loss: 0.45359888672828674 Improvement: 0.1417 +[3] (%Time%) 3 iterations Loss: 0.20857921242713928 Improvement: 0.2204 +[3] (%Time%) 4 iterations Loss: 0.16340915858745575 Improvement: 0.08846 +[3] (%Time%) 5 iterations Loss: 0.13761261105537415 Improvement: 0.04142 +[3] (%Time%) 6 iterations Loss: 0.12767525017261505 Improvement: 0.0178 +[3] (%Time%) 7 iterations Loss: 0.12204845249652863 Improvement: 0.00867 +[3] (%Time%) 8 iterations Loss: 0.1062392070889473 Improvement: 0.01402 +[3] (%Time%) 9 iterations Loss: 0.10295262187719345 Improvement: 0.005971 +[3] (%Time%) 10 iterations Loss: 0.10169977694749832 Improvement: 0.002432 +[3] (%Time%) 11 iterations Loss: 0.1010425016283989 Improvement: 0.001101 +[3] (%Time%) 12 iterations Loss: 0.09985871613025665 Improvement: 0.001163 +[3] (%Time%) 13 iterations Loss: 0.09840531647205353 Improvement: 0.001381 +[3] (%Time%) 14 iterations Loss: 0.09822732210159302 Improvement: 0.0004787 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Normalize #2' started. +[4] (%Time%) 362 examples +[4] 'Normalize #2' finished in %Time%. +[5] 'LBFGS data prep #2' started. +[5] 'LBFGS data prep #2' finished in %Time%. +[6] 'LBFGS Optimizer #2' started. +[6] (%Time%) 0 iterations Loss: 0.6931471824645996 +[6] (%Time%) 1 iterations Loss: 0.6230149269104004 Improvement: 0.07013 +[6] (%Time%) 2 iterations Loss: 0.5622537136077881 Improvement: 0.06264 +[6] (%Time%) 3 iterations Loss: 0.3308645784854889 Improvement: 0.1912 +[6] (%Time%) 4 iterations Loss: 0.18456830084323883 Improvement: 0.1574 +[6] (%Time%) 5 iterations Loss: 0.15308260917663574 Improvement: 0.06287 +[6] (%Time%) 6 iterations Loss: 0.13650739192962646 Improvement: 0.02814 +[6] (%Time%) 7 iterations Loss: 0.1291535645723343 Improvement: 0.01255 +[6] (%Time%) 8 iterations Loss: 0.12108723819255829 Improvement: 0.009187 +[6] (%Time%) 9 iterations Loss: 0.11698169261217117 Improvement: 0.005376 +[6] (%Time%) 10 iterations Loss: 0.11253994703292847 Improvement: 0.004675 +[6] (%Time%) 11 iterations Loss: 0.1117711216211319 Improvement: 0.001745 +[6] (%Time%) 12 iterations Loss: 0.111464723944664 Improvement: 0.0006662 +[6] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..97c98fa46c --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995858 0.964814 0.959113 0.938486 0.968967 0.977394 0.125259 0.865481 0.948366 0.991982 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=MeanVarNormalizer{col=Features} /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer.txt new file mode 100644 index 0000000000..23873e44ae --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 10.240182 0.9999643 5.150972346940456E-05 1 +6 0 -1.1200571 0.24600069 0.40736489593415492 0 +8 0 -3.9487858 0.01891348 0.027547723824485931 0 +9 0 -4.3041377 0.013332378 0.019363928162219202 0 +10 0 -4.9245176 0.0072138133 0.010445052394388471 0 +11 0 -5.023263 0.006539959 0.0094661543908224309 0 +18 1 6.1077223 0.9977793 0.0032073413870116912 1 +20 1 6.1394157 0.99784845 0.0031073729064209047 1 +21 1 5.79043 0.99695265 0.0044031048108654561 1 +25 1 0.83644867 0.6977167 0.51928670283371614 1 +28 0 -5.023263 0.006539959 0.0094661543908224309 0 +31 0 -4.5385065 0.010576305 0.015339644876315672 0 +32 1 5.9171133 0.9973143 0.003879895635620469 1 +35 0 -5.023263 0.006539959 0.0094661543908224309 0 +37 0 -1.3811293 0.2008277 0.32342152114162104 0 +40 0 ? ? ? 0 +41 1 1.9997759 0.88077354 0.18315695939082394 1 +44 1 6.1460505 0.99786264 0.0030868629883528767 1 +45 0 -4.9285326 0.007185116 0.010403350700356633 0 +46 1 3.6651945 0.9750398 0.036467015061163045 1 +48 0 -3.465179 0.0303194 0.044418474255629364 0 +50 1 1.7321749 0.8496904 0.23499086765459262 1 +51 1 -0.5320287 0.37004384 1.4342318788707105 0 +52 1 3.3840666 0.96720284 0.048109610770621473 1 +54 1 5.94145 0.99737865 0.0037867779125958421 1 +56 1 3.8859549 0.9798847 0.029316116563925798 1 +60 1 1.1627445 0.76183105 0.39245701495087315 1 +63 1 -0.60947704 0.3521785 1.50562120015828 0 +64 0 -5.3183413 0.004876975 0.0070532007598482728 0 +66 0 -3.94835 0.018921569 0.02755961949634533 0 +68 1 9.436265 0.99992025 0.00011506098160322404 1 +69 0 -4.7037826 0.008979576 0.013013304155307737 0 +70 0 -3.626728 0.025913702 0.037878503571533474 0 +71 1 7.2452755 0.99928695 0.001029081163103395 1 +72 0 -1.7539306 0.14755212 0.23031646280745757 0 +73 1 6.5814486 0.9986161 0.001997929380830338 1 +74 1 1.4721107 0.813378 0.29800216696333026 1 +76 0 -4.5513906 0.010442327 0.015144302269897698 0 +77 0 -3.638505 0.02561808 0.037440731183465928 0 +79 0 -4.93233 0.007158077 0.010364059762995995 0 +82 0 -3.5243025 0.028628604 0.041905091703152243 0 +88 0 -3.94835 0.018921569 0.02755961949634533 0 +90 0 -4.833585 0.007895114 0.011435443371134231 0 +91 0 -4.971357 0.0068859877 0.0099687421604731982 0 +92 0 -3.94835 0.018921569 0.02755961949634533 0 +93 0 -5.3183413 0.004876975 0.0070532007598482728 0 +95 0 -4.833585 0.007895114 0.011435443371134231 0 +96 0 -5.2664347 0.005135473 0.0074280107742848052 0 +97 0 -3.6532717 0.025252048 0.036898876335042144 0 +98 1 7.311146 0.99933237 0.00096351050700135713 1 +99 1 9.038476 0.99988127 0.00017130489018547468 1 +100 1 3.0386057 0.95428807 0.067503264882898095 1 +102 0 -3.6926994 0.024299512 0.035489744577618054 0 +104 1 10.246488 0.99996454 5.1165745927591405E-05 1 +105 1 2.0713453 0.88808674 0.17122750865212491 1 +106 1 7.1730757 0.9992336 0.0011061003722974585 1 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-5.5080204 0.0040377555 0.0058370421170140137 0 +144 0 -5.023263 0.006539959 0.0094661543908224309 0 +145 0 ? ? ? 0 +147 0 -4.814794 0.008043666 0.011651480971102541 0 +150 0 -4.9245176 0.0072138133 0.010445052394388471 0 +151 1 4.048602 0.9828524 0.024953319944645107 1 +152 1 7.404646 0.999392 0.00087746429875136403 1 +154 0 -5.8030987 0.0030091058 0.004347766745425947 0 +156 0 -4.9337225 0.007148188 0.010349690479271116 0 +161 0 -3.8185477 0.021487804 0.031338262622995577 0 +164 0 ? ? ? 0 +167 1 7.072035 0.99915224 0.0012235733412714462 1 +169 0 -5.6524463 0.003496653 0.0050534440765162749 0 +171 0 -4.833585 0.007895114 0.011435443371134231 0 +173 1 13.437144 0.99999857 2.0637932864940443E-06 1 +174 1 4.8828974 0.9924819 0.01088732137979174 1 +176 0 -4.5385065 0.010576305 0.015339644876315672 0 +177 1 3.7544203 0.97712165 0.033389906677476572 1 +179 1 1.6763983 0.842427 0.24737639282930343 1 +180 0 -4.9245176 0.0072138133 0.010445052394388471 0 +181 0 -5.8030987 0.0030091058 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a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..7b53b6b96e --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt @@ -0,0 +1,64 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=MeanVarNormalizer{col=Features} +Not adding a normalizer. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 229 | 10 | 0.9582 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9582 | 0.9775 | +OVERALL 0/1 ACCURACY: 0.970717 +LOG LOSS/instance: 0.110699 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.881479 +AUC: 0.996231 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996231 (0.0000) +Accuracy: 0.970717 (0.0000) +Positive precision: 0.958159 (0.0000) +Positive recall: 0.958159 (0.0000) +Negative precision: 0.977477 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.110699 (0.0000) +Log-loss reduction: 0.881479 (0.0000) +F1 Score: 0.958159 (0.0000) +AUPRC: 0.992209 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 699 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.6767677664756775 Improvement: 0.01638 +[3] (%Time%) 2 iterations Loss: 0.5105687975883484 Improvement: 0.1362 +[3] (%Time%) 3 iterations Loss: 0.29728010296821594 Improvement: 0.1949 +[3] (%Time%) 4 iterations Loss: 0.18677178025245667 Improvement: 0.1314 +[3] (%Time%) 5 iterations Loss: 0.13676075637340546 Improvement: 0.07029 +[3] (%Time%) 6 iterations Loss: 0.11334767192602158 Improvement: 0.03512 +[3] (%Time%) 7 iterations Loss: 0.10087242722511292 Improvement: 0.01814 +[3] (%Time%) 8 iterations Loss: 0.09591954201459885 Improvement: 0.008249 +[3] (%Time%) 9 iterations Loss: 0.09307149052619934 Improvement: 0.004198 +[3] (%Time%) 10 iterations Loss: 0.0926487147808075 Improvement: 0.001367 +[3] (%Time%) 11 iterations Loss: 0.09261518716812134 Improvement: 0.0003668 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..724e0193b5 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996231 0.970717 0.958159 0.958159 0.977477 0.977477 0.110699 0.881479 0.958159 0.992209 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=MeanVarNormalizer{col=Features} /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..562a6259f0 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.764402 0.022656268 0.033062046567057066 0 +1 0 2.1178465 0.8926257 3.219279227792275 1 +2 0 -4.3002224 0.013383981 0.019439383092732854 0 +3 0 1.351202 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--- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-out.txt @@ -0,0 +1,67 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 228 | 11 | 0.9540 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9580 | 0.9753 | +OVERALL 0/1 ACCURACY: 0.969253 +LOG LOSS/instance: 0.111002 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.881154 +AUC: 0.996136 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996136 (0.0000) +Accuracy: 0.969253 (0.0000) +Positive precision: 0.957983 (0.0000) +Positive recall: 0.953975 (0.0000) +Negative precision: 0.975281 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.111002 (0.0000) +Log-loss reduction: 0.881154 (0.0000) +F1 Score: 0.955975 (0.0000) +AUPRC: 0.991883 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831 +[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021 +[2] (%Time%) 3 iterations Loss: 0.5210472345352173 Improvement: 0.08851 +[2] (%Time%) 4 iterations Loss: 0.3810925781726837 Improvement: 0.1272 +[2] (%Time%) 5 iterations Loss: 0.3549964427947998 Improvement: 0.05131 +[2] (%Time%) 6 iterations Loss: 0.34571003913879395 Improvement: 0.01978 +[2] (%Time%) 7 iterations Loss: 0.3228526711463928 Improvement: 0.02209 +[2] (%Time%) 8 iterations Loss: 0.24733668565750122 Improvement: 0.06216 +[2] (%Time%) 9 iterations Loss: 0.22847387194633484 Improvement: 0.02969 +[2] (%Time%) 10 iterations Loss: 0.16646333038806915 Improvement: 0.05393 +[2] (%Time%) 11 iterations Loss: 0.12585707008838654 Improvement: 0.04394 +[2] (%Time%) 12 iterations Loss: 0.10631134361028671 Improvement: 0.02564 +[2] (%Time%) 13 iterations Loss: 0.09210390597581863 Improvement: 0.01707 +[2] (%Time%) 14 iterations Loss: 0.08548402786254883 Improvement: 0.009232 +[2] (%Time%) 15 iterations Loss: 0.08181773126125336 Improvement: 0.005058 +[2] (%Time%) 16 iterations Loss: 0.0811641737818718 Improvement: 0.001755 +[2] (%Time%) 17 iterations Loss: 0.0807165652513504 Improvement: 0.0007743 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..b39ecf37ed --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996136 0.969253 0.957983 0.953975 0.975281 0.977477 0.111002 0.881154 0.955975 0.991883 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-summary.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-summary.txt new file mode 100644 index 0000000000..e3ad368422 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer-summary.txt @@ -0,0 +1,12 @@ +Linear Binary Classification Predictor non-zero weights + +(Bias) -9.578454 +f0 0.57847303 +f5 0.40122226 +f6 0.38146052 +f3 0.36177918 +f8 0.23839118 +f7 0.2354545 +f4 0.11693436 +f1 0.115287125 +f2 0.10376115 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..96c79fa179 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.851943 0.02079674 0.030319734404703255 0 +1 0 2.683446 0.9360427 3.9667477413886059 1 +2 0 -4.607667 0.009876545 0.014319673324111819 0 +3 0 0.9931965 0.7297188 1.8874669526033945 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b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt new file mode 100644 index 0000000000..fa18ddb374 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt @@ -0,0 +1,67 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 198 | 41 | 0.8285 + negative || 3 | 441 | 0.9932 + ||====================== +Precision || 0.9851 | 0.9149 | +OVERALL 0/1 ACCURACY: 0.935578 +LOG LOSS/instance: 0.111002 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.881154 +AUC: 0.996136 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996136 (0.0000) +Accuracy: 0.935578 (0.0000) +Positive precision: 0.985075 (0.0000) +Positive recall: 0.828452 (0.0000) +Negative precision: 0.914938 (0.0000) +Negative recall: 0.993243 (0.0000) +Log-loss: 0.111002 (0.0000) +Log-loss reduction: 0.881154 (0.0000) +F1 Score: 0.900000 (0.0000) +AUPRC: 0.991883 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831 +[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021 +[2] (%Time%) 3 iterations Loss: 0.5210472345352173 Improvement: 0.08851 +[2] (%Time%) 4 iterations Loss: 0.3810925781726837 Improvement: 0.1272 +[2] (%Time%) 5 iterations Loss: 0.3549964427947998 Improvement: 0.05131 +[2] (%Time%) 6 iterations Loss: 0.34571003913879395 Improvement: 0.01978 +[2] (%Time%) 7 iterations Loss: 0.3228526711463928 Improvement: 0.02209 +[2] (%Time%) 8 iterations Loss: 0.24733668565750122 Improvement: 0.06216 +[2] (%Time%) 9 iterations Loss: 0.22847387194633484 Improvement: 0.02969 +[2] (%Time%) 10 iterations Loss: 0.16646333038806915 Improvement: 0.05393 +[2] (%Time%) 11 iterations Loss: 0.12585707008838654 Improvement: 0.04394 +[2] (%Time%) 12 iterations Loss: 0.10631134361028671 Improvement: 0.02564 +[2] (%Time%) 13 iterations Loss: 0.09210390597581863 Improvement: 0.01707 +[2] (%Time%) 14 iterations Loss: 0.08548402786254883 Improvement: 0.009232 +[2] (%Time%) 15 iterations Loss: 0.08181773126125336 Improvement: 0.005058 +[2] (%Time%) 16 iterations Loss: 0.0811641737818718 Improvement: 0.001755 +[2] (%Time%) 17 iterations Loss: 0.0807165652513504 Improvement: 0.0007743 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt new file mode 100644 index 0000000000..164bd04aa5 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996136 0.935578 0.985075 0.828452 0.914938 0.993243 0.111002 0.881154 0.9 0.991883 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt new file mode 100644 index 0000000000..79aba56a0b --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.851943 0.02079674 0.030319734404703255 0 +1 0 2.683446 0.9360427 3.9667477413886059 0 +2 0 -4.607667 0.009876545 0.014319673324111819 0 +3 0 0.9931965 0.7297188 1.8874669526033945 0 +4 0 -3.7068577 0.023966083 0.034996813287471229 0 +5 1 10.284526 0.99996585 4.9273870913609948E-05 1 +6 0 -2.5548348 0.072102346 0.10796240755799669 0 +7 0 -5.483601 0.0041371556 0.0059810346515218361 0 +8 0 -5.3967185 0.004510985 0.006522698994153339 0 +9 0 -4.69659 0.009043808 0.013106814588233793 0 +10 0 -6.2827697 0.0018647353 0.0026927557457968382 0 +11 0 -5.968823 0.0025507268 0.0036846220745495569 0 +12 1 -0.80001926 0.3100214 1.689560288227052 0 +13 0 -5.363391 0.004663146 0.0067432323413996785 0 +14 1 8.460618 0.9997884 0.00030530150678416689 1 +15 1 0.57511425 0.6399424 0.64398602102001679 0 +16 0 -4.811877 0.008066977 0.011685384746860946 0 +17 0 -4.430416 0.0117693655 0.017080315839062433 0 +18 1 6.6283293 0.9986794 0.0019064829346821315 1 +19 0 -3.2734704 0.03649261 0.053632360740655312 0 +20 1 7.0416136 0.9991261 0.0012613560580149764 1 +21 1 7.160178 0.9992237 0.0011203859514107531 1 +22 0 -5.39035 0.004539675 0.0065642778675289084 0 +23 1 ? ? ? 0 +24 0 -6.1658354 0.0020955629 0.0030264303088241035 0 +25 1 1.14081 0.75782835 0.4000569742868838 0 +26 0 -5.3919973 0.004532236 0.0065534968447901092 0 +27 0 -4.2334037 0.014295614 0.020773049972623758 0 +28 0 -5.968823 0.0025507268 0.0036846220745495569 0 +29 0 -6.721234 0.0012036001 0.0017374736946093711 0 +30 0 -5.5072846 0.0040407153 0.0058413294412794697 0 +31 0 -5.5873623 0.0037309208 0.00539264703325237 0 +32 1 6.093754 0.99774814 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-5.7718105 0.0031044467 0.0044857363153125709 0 +693 0 -5.1519585 0.0057547493 0.0083263292769673487 0 +694 0 -5.2536535 0.0052011875 0.0075233094172650833 0 +695 0 -6.350283 0.0017432085 0.0025171128231916808 0 +696 1 4.4949503 0.98895806 0.016018753724303419 1 +697 1 3.1488342 0.9588628 0.060603722373998026 1 +698 1 3.7653837 0.97736543 0.033030012232047976 1 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..c96fe96c1e --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-out.txt @@ -0,0 +1,99 @@ +maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=BinNormalizer{col=Features numBins=5} +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 9 of 10 weights. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 129 | 5 | 0.9627 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9485 | 0.9771 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.145463 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.848001 +AUC: 0.992232 +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 || 99 | 6 | 0.9429 + negative || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9706 | 0.9736 | +OVERALL 0/1 ACCURACY: 0.972644 +LOG LOSS/instance: 0.123323 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.863498 +AUC: 0.996769 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.994500 (0.0023) +Accuracy: 0.969373 (0.0033) +Positive precision: 0.959559 (0.0110) +Positive recall: 0.952772 (0.0099) +Negative precision: 0.975316 (0.0017) +Negative recall: 0.977394 (0.0092) +Log-loss: 0.134393 (0.0111) +Log-loss reduction: 0.855749 (0.0077) +F1 Score: 0.956039 (0.0005) +AUPRC: 0.988987 (0.0037) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 337 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.46772801876068115 Improvement: 0.2254 +[3] (%Time%) 2 iterations Loss: 0.29355108737945557 Improvement: 0.1844 +[3] (%Time%) 3 iterations Loss: 0.13735580444335938 Improvement: 0.1629 +[3] (%Time%) 4 iterations Loss: 0.13044576346874237 Improvement: 0.04545 +[3] (%Time%) 5 iterations Loss: 0.12229779362678528 Improvement: 0.01745 +[3] (%Time%) 6 iterations Loss: 0.11961209774017334 Improvement: 0.006373 +[3] (%Time%) 7 iterations Loss: 0.11694043129682541 Improvement: 0.003597 +[3] (%Time%) 8 iterations Loss: 0.11432048678398132 Improvement: 0.002864 +[3] (%Time%) 9 iterations Loss: 0.11364643275737762 Improvement: 0.001222 +[3] (%Time%) 10 iterations Loss: 0.11349356174468994 Improvement: 0.00042 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Normalize #2' started. +[4] (%Time%) 362 examples +[4] 'Normalize #2' finished in %Time%. +[5] 'LBFGS data prep #2' started. +[5] 'LBFGS data prep #2' finished in %Time%. +[6] 'LBFGS Optimizer #2' started. +[6] (%Time%) 0 iterations Loss: 0.6931471824645996 +[6] (%Time%) 1 iterations Loss: 0.4943659007549286 Improvement: 0.1988 +[6] (%Time%) 2 iterations Loss: 0.24648860096931458 Improvement: 0.2381 +[6] (%Time%) 3 iterations Loss: 0.18160340189933777 Improvement: 0.1061 +[6] (%Time%) 4 iterations Loss: 0.15490023791790009 Improvement: 0.04632 +[6] (%Time%) 5 iterations Loss: 0.14409959316253662 Improvement: 0.01966 +[6] (%Time%) 6 iterations Loss: 0.13997505605220795 Improvement: 0.008004 +[6] (%Time%) 7 iterations Loss: 0.13867685198783875 Improvement: 0.002974 +[6] (%Time%) 8 iterations Loss: 0.13838209211826324 Improvement: 0.0009646 +[6] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..224df3f559 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.9945 0.969373 0.959559 0.952772 0.975316 0.977394 0.134393 0.855749 0.956039 0.988987 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 xf=BinNormalizer{col=Features numBins=5} /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer.txt new file mode 100644 index 0000000000..6df366c6b6 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 6.3731 0.998296 0.0024604168548522432 1 +6 0 -1.5907521 0.1692781 0.26756250697979528 0 +8 0 -5.2236195 0.00535892 0.0077520772435321107 0 +9 0 -4.47695 0.011240251 0.016308081037055566 0 +10 0 -4.955529 0.0069950754 0.010127222346273152 0 +11 0 -5.209665 0.005433817 0.0078607169784834265 0 +18 1 4.253465 0.9859843 0.020363383207649043 1 +20 1 3.8221421 0.9785876 0.031227052445240006 1 +21 1 4.1076407 0.9838196 0.023534291913581692 1 +25 1 1.3780742 0.7986815 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+695 0 -4.991929 0.006746721 0.0097664438192888777 0 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..1f5287c5e2 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt @@ -0,0 +1,62 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=BinNormalizer{col=Features numBins=5} +Not adding a normalizer. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 9 of 10 weights. +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 || 232 | 7 | 0.9707 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9587 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.116898 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874842 +AUC: 0.995208 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995208 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.958678 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984127 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.116898 (0.0000) +Log-loss reduction: 0.874842 (0.0000) +F1 Score: 0.964657 (0.0000) +AUPRC: 0.990065 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 699 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.47700226306915283 Improvement: 0.2161 +[3] (%Time%) 2 iterations Loss: 0.18854373693466187 Improvement: 0.274 +[3] (%Time%) 3 iterations Loss: 0.1417279690504074 Improvement: 0.1009 +[3] (%Time%) 4 iterations Loss: 0.12406111508607864 Improvement: 0.03823 +[3] (%Time%) 5 iterations Loss: 0.11358059197664261 Improvement: 0.0174 +[3] (%Time%) 6 iterations Loss: 0.10794322192668915 Improvement: 0.008575 +[3] (%Time%) 7 iterations Loss: 0.10557964444160461 Improvement: 0.003916 +[3] (%Time%) 8 iterations Loss: 0.10531868040561676 Improvement: 0.001175 +[3] (%Time%) 9 iterations Loss: 0.10510236769914627 Improvement: 0.0004559 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..460915d564 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995208 0.97511 0.958678 0.970711 0.984127 0.977477 0.116898 0.874842 0.964657 0.990065 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 xf=BinNormalizer{col=Features numBins=5} /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5ecd3a79da --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.9925442 0.018118374 0.026378988828395827 0 +1 0 2.2560353 0.90516984 3.3985102900536521 1 +2 0 -4.274502 0.013727903 0.019942376975914694 0 +3 0 2.4613528 0.92138773 3.6691017174958245 1 +4 0 -4.2323236 0.014310841 0.02079533684323219 0 +5 1 6.468641 0.99845105 0.0022363890442921863 1 +6 0 -2.3718886 0.0853416 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threads=- norm=No dout=%Output% data=%Data% seed=1 +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 9 of 10 weights. +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 || 129 | 5 | 0.9627 + negative || 8 | 212 | 0.9636 + ||====================== +Precision || 0.9416 | 0.9770 | +OVERALL 0/1 ACCURACY: 0.963277 +LOG LOSS/instance: 0.140963 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.852703 +AUC: 0.994437 +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 || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9697 | 0.9609 | +OVERALL 0/1 ACCURACY: 0.963526 +LOG LOSS/instance: 0.111875 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.876170 +AUC: 0.997066 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995752 (0.0013) +Accuracy: 0.963401 (0.0001) +Positive precision: 0.955651 (0.0140) +Positive recall: 0.938486 (0.0242) +Negative precision: 0.968914 (0.0080) +Negative recall: 0.975122 (0.0115) +Log-loss: 0.126419 (0.0145) +Log-loss reduction: 0.864436 (0.0117) +F1 Score: 0.946603 (0.0054) +AUPRC: 0.991761 (0.0020) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387 +[2] (%Time%) 2 iterations Loss: 0.642568051815033 Improvement: 0.01725 +[2] (%Time%) 3 iterations Loss: 0.6358204483985901 Improvement: 0.009247 +[2] (%Time%) 4 iterations Loss: 0.6251663565635681 Improvement: 0.01031 +[2] (%Time%) 5 iterations Loss: 0.5822214484214783 Improvement: 0.03481 +[2] (%Time%) 6 iterations Loss: 0.24095411598682404 Improvement: 0.2647 +[2] (%Time%) 7 iterations Loss: 0.18065451085567474 Improvement: 0.1114 +[2] (%Time%) 8 iterations Loss: 0.12022408843040466 Improvement: 0.07317 +[2] (%Time%) 9 iterations Loss: 0.09626226872205734 Improvement: 0.03626 +[2] (%Time%) 10 iterations Loss: 0.08481403440237045 Improvement: 0.01765 +[2] (%Time%) 11 iterations Loss: 0.07749200612306595 Improvement: 0.009905 +[2] (%Time%) 12 iterations Loss: 0.07508934289216995 Improvement: 0.004278 +[2] (%Time%) 13 iterations Loss: 0.07309532910585403 Improvement: 0.002565 +[2] (%Time%) 14 iterations Loss: 0.07258453965187073 Improvement: 0.001024 +[2] (%Time%) 15 iterations Loss: 0.07185003906488419 Improvement: 0.000807 +[2] (%Time%) 16 iterations Loss: 0.07156690955162048 Improvement: 0.0004141 +[2] (%Time%) 17 iterations Loss: 0.07156465202569962 Improvement: 0.0001052 +[2] (%Time%) 18 iterations Loss: 0.07146970927715302 Improvement: 9.751E-05 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'LBFGS data prep #2' started. +[3] 'LBFGS data prep #2' finished in %Time%. +[4] 'LBFGS Optimizer #2' started. +[4] (%Time%) 0 iterations Loss: 0.6931471824645996 +[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603 +[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156 +[4] (%Time%) 3 iterations Loss: 0.6134979724884033 Improvement: 0.01083 +[4] (%Time%) 4 iterations Loss: 0.6002617478370667 Improvement: 0.01264 +[4] (%Time%) 5 iterations Loss: 0.5936987996101379 Improvement: 0.008078 +[4] (%Time%) 6 iterations Loss: 0.5855143666267395 Improvement: 0.008158 +[4] (%Time%) 7 iterations Loss: 0.5579032301902771 Improvement: 0.02275 +[4] (%Time%) 8 iterations Loss: 0.514010488986969 Improvement: 0.03861 +[4] (%Time%) 9 iterations Loss: 0.4704664349555969 Improvement: 0.04231 +[4] (%Time%) 10 iterations Loss: 0.3519382178783417 Improvement: 0.09947 +[4] (%Time%) 11 iterations Loss: 0.24160803854465485 Improvement: 0.1076 +[4] (%Time%) 12 iterations Loss: 0.15622469782829285 Improvement: 0.09094 +[4] (%Time%) 13 iterations Loss: 0.13706165552139282 Improvement: 0.03711 +[4] (%Time%) 14 iterations Loss: 0.12257832288742065 Improvement: 0.02014 +[4] (%Time%) 15 iterations Loss: 0.11211910843849182 Improvement: 0.01288 +[4] (%Time%) 16 iterations Loss: 0.10575487464666367 Improvement: 0.007993 +[4] (%Time%) 17 iterations Loss: 0.10021656006574631 Improvement: 0.006152 +[4] (%Time%) 18 iterations Loss: 0.09685001522302628 Improvement: 0.004063 +[4] (%Time%) 19 iterations Loss: 0.0928599014878273 Improvement: 0.004008 +[4] (%Time%) 20 iterations Loss: 0.09131418913602829 Improvement: 0.002161 +[4] (%Time%) 21 iterations Loss: 0.09053906053304672 Improvement: 0.001122 +[4] (%Time%) 22 iterations Loss: 0.09002906829118729 Improvement: 0.0006629 +[4] (%Time%) 23 iterations Loss: 0.08863715082406998 Improvement: 0.00121 +[4] (%Time%) 24 iterations Loss: 0.0881308913230896 Improvement: 0.0006821 +[4] (%Time%) 25 iterations Loss: 0.08750966191291809 Improvement: 0.0006364 +[4] (%Time%) 26 iterations Loss: 0.08728768676519394 Improvement: 0.0003256 +[4] (%Time%) 27 iterations Loss: 0.08721958100795746 Improvement: 0.0001325 +[4] (%Time%) 28 iterations Loss: 0.08721008896827698 Improvement: 4.024E-05 +[4] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..5a076145d7 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995752 0.963401 0.955651 0.938486 0.968914 0.975122 0.126419 0.864436 0.946603 0.991761 0.1 0.0001 1 + LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+ + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt new file mode 100644 index 0000000000..38ba3a740f --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 12.634016 0.9999967 4.7295306510363196E-06 1 +6 0 -1.3840265 0.2003631 0.32258304597672599 0 +8 0 -4.72443 0.008797685 0.01274853746288432 0 +9 0 -5.1366463 0.005843026 0.0084544286227711037 0 +10 0 -5.6093 0.0036502592 0.0052758458554607087 0 +11 0 -5.8696203 0.002815993 0.0040683501047807172 0 +18 1 7.267023 0.9993023 0.001006879741116309 1 +20 1 7.564518 0.99948174 0.00074788839258190052 1 +21 1 7.3895073 0.9993827 0.0008908871693913803 1 +25 1 1.3487854 0.793931 0.33291445228970418 1 +28 0 -5.8696203 0.002815993 0.0040683501047807172 0 +31 0 -5.1809826 0.0055910437 0.0080888046329900768 0 +32 1 7.291664 0.99931926 0.00098244136155410146 1 +35 0 -5.8696203 0.002815993 0.0040683501047807172 0 +37 0 -1.0239582 0.2642571 0.44272638610457016 0 +40 0 ? ? ? 0 +41 1 2.784809 0.9418494 0.086431684776605053 1 +44 1 7.8207245 0.9995988 0.00057892375102128462 1 +45 0 -5.7771015 0.003088115 0.0044621015121553741 0 +46 1 4.363323 0.9874242 0.018258099080425954 1 +48 0 -3.8101325 0.021665458 0.031600214495233307 0 +50 1 2.3506079 0.9129825 0.1313408508726798 1 +51 1 -0.38570404 0.4047519 1.3048902531945354 0 +52 1 4.7617655 0.991522 0.012283290824972246 1 +54 1 7.3694706 0.9993702 0.00090887057002802098 1 +56 1 4.514043 0.9891646 0.015717498447270977 1 +60 1 2.0933666 0.89025676 0.16770660536078968 1 +63 1 -0.43266487 0.39349017 1.345600516084799 0 +64 0 -6.2355585 0.0019547066 0.0028228053227955028 0 +66 0 -4.4491057 0.011553961 0.016765885815999977 0 +68 1 12.42049 0.99999595 5.8474219794828371E-06 1 +69 0 -5.372943 0.0046190205 0.0066792757321367446 0 +70 0 -3.944561 0.018992033 0.027663241712138437 0 +71 1 8.002766 0.99966556 0.00048257802859336444 1 +72 0 -1.4694653 0.18702391 0.29871516855965657 0 +73 1 8.232639 0.9997342 0.00038348628329455832 1 +74 1 1.5337801 0.8225587 0.28180945594824713 1 +76 0 -4.994693 0.006728226 0.0097395799853625745 0 +77 0 -4.450124 0.01154234 0.016748924320874031 0 +79 0 -5.807241 0.0029967043 0.0043298212867696499 0 +82 0 -4.08207 0.016592547 0.024138806297648339 0 +88 0 -4.4491057 0.011553961 0.016765885815999977 0 +90 0 -5.546921 0.0038842994 0.0056147713181484132 0 +91 0 -5.8728333 0.0028069853 0.0040553180261770671 0 +92 0 -4.4491057 0.011553961 0.016765885815999977 0 +93 0 -6.2355585 0.0019547066 0.0028228053227955028 0 +95 0 -5.546921 0.0038842994 0.0056147713181484132 0 +96 0 -6.238772 0.0019484477 0.0028137579050843478 0 +97 0 -4.083167 0.016574655 0.024112557476670533 0 +98 1 8.655586 0.9998259 0.00025120253004900898 1 +99 1 11.468365 0.99998957 1.5048560434990871E-05 1 +100 1 3.7766733 0.97761387 0.032663346614426464 1 +102 0 -4.0354834 0.01737008 0.025279927497574458 0 +104 1 12.723648 0.999997 4.2995726784359523E-06 1 +105 1 2.7624168 0.94061077 0.088330249233447317 1 +106 1 8.566938 0.99980974 0.00027451042551103868 1 +108 0 -5.8876033 0.0027659447 0.0039959435227908723 0 +109 1 7.138527 0.9992067 0.0011449127273942801 1 +111 1 2.7874622 0.94199455 0.086209385178054376 1 +112 1 9.633659 0.9999345 9.4507562065679747E-05 1 +113 1 9.298004 0.9999084 0.00013217472169822292 1 +115 0 -5.0740213 0.006218298 0.0089991161843039711 0 +117 1 11.032467 0.99998385 2.3303837404214046E-05 1 +120 0 -4.8138123 0.008051504 0.011662880569639303 0 +121 0 -3.9854646 0.018244753 0.026564690979039515 0 +122 1 9.10256 0.99988866 0.00016064073906035849 1 +123 1 3.8359165 0.9788744 0.030804357887786005 1 +125 0 -6.2355585 0.0019547066 0.0028228053227955028 0 +128 1 3.930893 0.98075163 0.028040261476875789 1 +129 0 -6.4251633 0.0016176477 0.0023356619743196646 0 +131 0 -5.1809826 0.0055910437 0.0080888046329900768 0 +132 1 8.021416 0.99967176 0.00047363196654040616 1 +133 0 -5.3297043 0.004822137 0.0069737005123077563 0 +137 0 -6.19232 0.002040903 0.0029474094708446014 0 +138 0 -4.7241216 0.008800375 0.012752453623473303 0 +141 0 -6.5582585 0.0014163447 0.0020448018331478031 0 +144 0 -5.8696203 0.002815993 0.0040683501047807172 0 +145 0 ? ? ? 0 +147 0 -5.7106357 0.0032996438 0.0047682507744472441 0 +150 0 -5.6093 0.0036502592 0.0052758458554607087 0 +151 1 5.1192083 0.9940548 0.0086027166383069592 1 +152 1 9.233112 0.99990225 0.00014103266690546063 1 +154 0 -6.9241967 0.0009827262 0.0014184713509792089 0 +156 0 -5.820839 0.0029563506 0.00427142933878765 0 +161 0 -4.275128 0.013719428 0.019929979951712175 0 +164 0 ? ? ? 0 +167 1 8.727816 0.999838 0.00023374335687970695 1 +169 0 -6.6670895 0.0012704796 0.0018340800023318104 0 +171 0 -5.546921 0.0038842994 0.0056147713181484132 0 +173 1 16.564802 0.99999994 8.5991327994145617E-08 1 +174 1 5.9242706 0.99733335 0.0038523045725836797 1 +176 0 -5.1809826 0.0055910437 0.0080888046329900768 0 +177 1 4.522547 0.98925537 0.01558510513199726 1 +179 1 1.797656 0.85786337 0.22118020942368269 1 +180 0 -5.6093 0.0036502592 0.0052758458554607087 0 +181 0 -6.9241967 0.0009827262 0.0014184713509792089 0 +183 1 9.378704 0.9999155 0.00012194085271734281 1 +187 1 15.031347 0.9999997 4.2995669122556443E-07 1 +188 1 9.163334 0.9998952 0.00015118067097744242 1 +189 0 -4.7185793 0.0088488525 0.012823014112422093 0 +191 1 11.084356 0.9999846 2.2185932549356626E-05 1 +192 0 -4.7718053 0.008394028 0.012161134816015745 0 +196 0 6.867628 0.99896014 9.9093913256587243 1 +198 0 -6.9241967 0.0009827262 0.0014184713509792089 0 +199 0 -5.503682 0.0040552393 0.0058623682734960988 0 +201 1 9.713139 0.9999395 8.7283835619421935E-05 1 +202 0 -5.546921 0.0038842994 0.0056147713181484132 0 +204 0 -5.546921 0.0038842994 0.0056147713181484132 0 +205 1 12.771791 0.99999714 4.1275895252744015E-06 1 +206 1 4.562334 0.98967016 0.014980318711755168 1 +207 0 -5.6093 0.0036502592 0.0052758458554607087 0 +209 0 -4.1455464 0.015587949 0.02266577604738473 0 +210 1 15.273811 0.99999976 3.4396534272948301E-07 1 +211 1 9.317268 0.9999102 0.00012959474788452077 1 +212 0 -5.546921 0.0038842994 0.0056147713181484132 0 +216 0 -6.2355585 0.0019547066 0.0028228053227955028 0 +218 1 7.7958946 0.9995887 0.00059346219104913593 1 +219 0 -2.889985 0.05265086 0.078031876546262244 0 +223 1 5.9368534 0.9973666 0.0038041939186728327 1 +226 1 10.31377 0.9999668 4.7897963370785976E-05 1 +228 0 -5.6093 0.0036502592 0.0052758458554607087 0 +233 1 5.7209215 0.996734 0.0047195198090090517 1 +237 1 5.9034386 0.9972774 0.003933268595803863 1 +239 1 5.3055105 0.9950604 0.0071440180264157188 1 +240 0 -2.4950886 0.076203205 0.11435255346380756 0 +241 0 -4.23845 0.014224679 0.020669231770834882 0 +242 0 -5.1809826 0.0055910437 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0.0019547066 0.0028228053227955028 0 +278 0 -5.546921 0.0038842994 0.0056147713181484132 0 +279 1 7.2664165 0.99930185 0.0010075681521692589 1 +280 0 -4.8150444 0.00804167 0.011648576905263216 0 +283 1 5.7060537 0.99668527 0.0047900929238417272 1 +284 1 7.0210648 0.9991079 0.0012876065918351616 1 +285 1 14.94092 0.9999997 4.2995669122556443E-07 1 +288 1 2.0280247 0.88370824 0.17835796079086519 1 +290 0 -6.9241967 0.0009827262 0.0014184713509792089 0 +291 0 -5.546921 0.0038842994 0.0056147713181484132 0 +293 1 4.410328 0.9879947 0.017424834791133156 1 +296 0 1.5063057 0.81851304 2.4620621682764696 1 +297 0 ? ? ? 0 +299 1 7.9361706 0.99964255 0.00051578216702891913 1 +300 1 7.851227 0.9996109 0.00056146061141647502 1 +301 0 -5.546921 0.0038842994 0.0056147713181484132 0 +303 0 -5.546921 0.0038842994 0.0056147713181484132 0 +304 1 5.3868313 0.9954444 0.0065873329922731634 1 +308 1 7.82181 0.9995993 0.00057823554447422978 1 +309 0 -1.5875931 0.16972281 0.26833503153858346 0 +311 0 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0.0028216895 0.004076591553961454 0 +673 0 -4.2515373 0.014042326 0.020402380515903241 0 +674 0 -6.8532095 0.0010549467 0.0015227697088502346 0 +675 0 -4.2696095 0.0137943 0.020039503854497525 0 +676 0 -6.5118427 0.0014835353 0.0021418781570889814 0 +677 0 -4.6574306 0.009401588 0.013627786320506213 0 +684 0 -7.2025456 0.00074413355 0.0010739574120585704 0 +686 0 -7.2025456 0.00074413355 0.0010739574120585704 0 +687 0 -5.156148 0.005730828 0.0082916185312840122 0 +690 0 -6.6232843 0.0013272946 0.0019161533025761086 0 +695 0 -6.566267 0.0014050631 0.0020285029665137564 0 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..931f668bc7 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt @@ -0,0 +1,71 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 228 | 11 | 0.9540 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9580 | 0.9753 | +OVERALL 0/1 ACCURACY: 0.969253 +LOG LOSS/instance: 0.109007 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.883291 +AUC: 0.996287 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996287 (0.0000) +Accuracy: 0.969253 (0.0000) +Positive precision: 0.957983 (0.0000) +Positive recall: 0.953975 (0.0000) +Negative precision: 0.975281 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.109007 (0.0000) +Log-loss reduction: 0.883291 (0.0000) +F1 Score: 0.955975 (0.0000) +AUPRC: 0.992293 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'LBFGS data prep' started. +[1] 'LBFGS data prep' finished in %Time%. +[2] 'LBFGS Optimizer' started. +[2] (%Time%) 0 iterations Loss: 0.6931471824645996 +[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831 +[2] (%Time%) 2 iterations Loss: 0.6306551694869995 Improvement: 0.021 +[2] (%Time%) 3 iterations Loss: 0.6191107034683228 Improvement: 0.0138 +[2] (%Time%) 4 iterations Loss: 0.6139997839927673 Improvement: 0.007257 +[2] (%Time%) 5 iterations Loss: 0.6017453074455261 Improvement: 0.01101 +[2] (%Time%) 6 iterations Loss: 0.5552627444267273 Improvement: 0.03762 +[2] (%Time%) 7 iterations Loss: 0.4080789387226105 Improvement: 0.1198 +[2] (%Time%) 8 iterations Loss: 0.22476643323898315 Improvement: 0.1674 +[2] (%Time%) 9 iterations Loss: 0.17795847356319427 Improvement: 0.07696 +[2] (%Time%) 10 iterations Loss: 0.137373685836792 Improvement: 0.04968 +[2] (%Time%) 11 iterations Loss: 0.1169188842177391 Improvement: 0.02776 +[2] (%Time%) 12 iterations Loss: 0.1018657311797142 Improvement: 0.01823 +[2] (%Time%) 13 iterations Loss: 0.0918111726641655 Improvement: 0.0121 +[2] (%Time%) 14 iterations Loss: 0.08791256695985794 Improvement: 0.005949 +[2] (%Time%) 15 iterations Loss: 0.08431504666805267 Improvement: 0.004185 +[2] (%Time%) 16 iterations Loss: 0.0827585905790329 Improvement: 0.002214 +[2] (%Time%) 17 iterations Loss: 0.0813479945063591 Improvement: 0.001611 +[2] (%Time%) 18 iterations Loss: 0.08036942780017853 Improvement: 0.001137 +[2] (%Time%) 19 iterations Loss: 0.07970958948135376 Improvement: 0.0007791 +[2] (%Time%) 20 iterations Loss: 0.07952725142240524 Improvement: 0.0003315 +[2] (%Time%) 21 iterations Loss: 0.07951106876134872 Improvement: 9.502E-05 +[2] 'LBFGS Optimizer' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..c317b4e4da --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996287 0.969253 0.957983 0.953975 0.975281 0.977477 0.109007 0.883291 0.955975 0.992293 0.1 0.0001 1 + LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+ + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5e885ac32a --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-non-negative-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.9978414 0.018024378 0.026240884777892562 0 +1 0 2.2570543 0.9052573 3.3998411691279338 1 +2 0 -4.68605 0.009138758 0.013245055557552017 0 +3 0 1.2745533 0.7815212 2.1944348071729731 1 +4 0 -3.8983564 0.019872293 0.028958355424267362 0 +5 1 10.383684 0.99996907 4.4630188215668621E-05 1 +6 0 -2.6780772 0.06427943 0.095850325661809468 0 +7 0 -5.310401 0.0049156626 0.0071092898061758357 0 +8 0 -4.9831486 0.0068058167 0.009852282525592778 0 +9 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0.0065183711781357278 0 +689 0 -4.404127 0.012079086 0.017532540698851264 0 +690 0 -6.371276 0.001707057 0.0024648671060441625 0 +691 1 2.8413372 0.94486916 0.081813526767298625 1 +692 0 -5.9340725 0.0026406858 0.0038147432465966695 0 +693 0 -5.130742 0.005877423 0.0085043455317272768 0 +694 0 -5.4755683 0.004170383 0.0060291714053851949 0 +695 0 -6.4707613 0.001545654 0.0022316325112485642 0 +696 1 5.1025124 0.9939553 0.0087471017414342198 1 +697 1 3.355402 0.9662813 0.049484860722445317 1 +698 1 4.2769833 0.98630565 0.019893290833474131 1 diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..7079f4722b --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-out.txt @@ -0,0 +1,97 @@ +maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +Not training a calibrator because it is not needed. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 8 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 8 of 10 weights. +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 || 129 | 5 | 0.9627 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9485 | 0.9771 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.137058 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.856784 +AUC: 0.994166 +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 || 3 | 221 | 0.9866 + ||====================== +Precision || 0.9697 | 0.9609 | +OVERALL 0/1 ACCURACY: 0.963526 +LOG LOSS/instance: 0.130675 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.855361 +AUC: 0.997279 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995722 (0.0016) +Accuracy: 0.964814 (0.0013) +Positive precision: 0.959113 (0.0106) +Positive recall: 0.938486 (0.0242) +Negative precision: 0.968967 (0.0081) +Negative recall: 0.977394 (0.0092) +Log-loss: 0.133866 (0.0032) +Log-loss reduction: 0.856072 (0.0007) +F1 Score: 0.948366 (0.0072) +AUPRC: 0.991520 (0.0025) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 337 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.5082830786705017 Improvement: 0.1849 +[3] (%Time%) 2 iterations Loss: 0.22038933634757996 Improvement: 0.2673 +[3] (%Time%) 3 iterations Loss: 0.15389013290405273 Improvement: 0.1143 +[3] (%Time%) 4 iterations Loss: 0.14956159889698029 Improvement: 0.0315 +[3] (%Time%) 5 iterations Loss: 0.14420396089553833 Improvement: 0.01187 +[3] (%Time%) 6 iterations Loss: 0.14081034064292908 Improvement: 0.005512 +[3] (%Time%) 7 iterations Loss: 0.13886460661888123 Improvement: 0.002837 +[3] (%Time%) 8 iterations Loss: 0.1385388970375061 Improvement: 0.0009535 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Normalize #2' started. +[4] (%Time%) 362 examples +[4] 'Normalize #2' finished in %Time%. +[5] 'LBFGS data prep #2' started. +[5] 'LBFGS data prep #2' finished in %Time%. +[6] 'LBFGS Optimizer #2' started. +[6] (%Time%) 0 iterations Loss: 0.6931471824645996 +[6] (%Time%) 1 iterations Loss: 0.517149806022644 Improvement: 0.176 +[6] (%Time%) 2 iterations Loss: 0.22123011946678162 Improvement: 0.2719 +[6] (%Time%) 3 iterations Loss: 0.17534516751766205 Improvement: 0.09971 +[6] (%Time%) 4 iterations Loss: 0.1547054648399353 Improvement: 0.04017 +[6] (%Time%) 5 iterations Loss: 0.14621466398239136 Improvement: 0.01639 +[6] (%Time%) 6 iterations Loss: 0.14243879914283752 Improvement: 0.006927 +[6] (%Time%) 7 iterations Loss: 0.141435906291008 Improvement: 0.002484 +[6] (%Time%) 8 iterations Loss: 0.14130792021751404 Improvement: 0.0007169 +[6] 'LBFGS Optimizer #2' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..7e0d838080 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995722 0.964814 0.959113 0.938486 0.968967 0.977394 0.133866 0.856072 0.948366 0.99152 0.1 0.001 1 LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer.txt new file mode 100644 index 0000000000..d48af18cb9 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 8.195853 0.99972427 0.00039785072387288643 1 +6 0 -0.92675495 0.28358352 0.48112957403187329 0 +8 0 -4.441124 0.011645473 0.016899458781483138 0 +9 0 -3.5258589 0.028585356 0.041840859443891751 0 +10 0 -4.437841 0.011683321 0.016954706359182743 0 +11 0 -4.2941904 0.013463867 0.019556202484947195 0 +18 1 5.0770636 0.99380046 0.0089718833435623487 1 +20 1 3.5104084 0.9709825 0.042482812606117214 1 +21 1 4.914091 0.9927111 0.010554131975312969 1 +25 1 0.77733994 0.6851065 0.54559978857802161 1 +28 0 -4.2941904 0.013463867 0.019556202484947195 0 +31 0 -4.078273 0.016654618 0.02422986930767649 0 +32 1 4.2802258 0.9863494 0.019829298264044417 1 +35 0 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a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..c1672f5a8c --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-out.txt @@ -0,0 +1,64 @@ +maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Warning: Skipped 16 instances with missing features/label/weight during training +Beginning optimization +num vars: 10 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 10 weights. +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 || 228 | 11 | 0.9540 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9620 | 0.9753 | +OVERALL 0/1 ACCURACY: 0.970717 +LOG LOSS/instance: 0.119042 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.872546 +AUC: 0.996108 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996108 (0.0000) +Accuracy: 0.970717 (0.0000) +Positive precision: 0.962025 (0.0000) +Positive recall: 0.953975 (0.0000) +Negative precision: 0.975336 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.119042 (0.0000) +Log-loss reduction: 0.872546 (0.0000) +F1 Score: 0.957983 (0.0000) +AUPRC: 0.992030 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 699 examples +[1] 'Normalize' finished in %Time%. +[2] 'LBFGS data prep' started. +[2] 'LBFGS data prep' finished in %Time%. +[3] 'LBFGS Optimizer' started. +[3] (%Time%) 0 iterations Loss: 0.6931471824645996 +[3] (%Time%) 1 iterations Loss: 0.503106415271759 Improvement: 0.19 +[3] (%Time%) 2 iterations Loss: 0.42320528626441956 Improvement: 0.1019 +[3] (%Time%) 3 iterations Loss: 0.16024325788021088 Improvement: 0.2246 +[3] (%Time%) 4 iterations Loss: 0.13900212943553925 Improvement: 0.07149 +[3] (%Time%) 5 iterations Loss: 0.12618772685527802 Improvement: 0.02744 +[3] (%Time%) 6 iterations Loss: 0.12205933779478073 Improvement: 0.009952 +[3] (%Time%) 7 iterations Loss: 0.11975764483213425 Improvement: 0.004214 +[3] (%Time%) 8 iterations Loss: 0.11760365962982178 Improvement: 0.002669 +[3] (%Time%) 9 iterations Loss: 0.11399238556623459 Improvement: 0.003376 +[3] (%Time%) 10 iterations Loss: 0.11343003809452057 Improvement: 0.001266 +[3] (%Time%) 11 iterations Loss: 0.11329112946987152 Improvement: 0.0004206 +[3] 'LBFGS Optimizer' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..627d55f385 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LogisticRegression +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996108 0.970717 0.962025 0.953975 0.975336 0.97973 0.119042 0.872546 0.957983 0.99203 0.1 0.001 1 LogisticRegression %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=%Output% data=%Data% out=%Output% seed=1 /l2:0.1;/ot:0.001;/nt:1 + diff --git a/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..899a3d3172 --- /dev/null +++ b/test/BaselineOutput/Common/LogisticRegression/win-arm/LogisticRegression-norm-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.4762254 0.029996313 0.043937864099631886 0 +1 0 1.9774284 0.87840676 3.0398651072956344 1 +2 0 -3.93716 0.019130416 0.027866766122312772 0 +3 0 1.9661341 0.87719524 3.0255616022478864 1 +4 0 -3.5451653 0.028054101 0.041052083508060465 0 +5 1 8.377454 0.99977005 0.00033179268362335749 1 +6 0 -1.9164152 0.12826185 0.19803324588790117 0 +7 0 -4.4696636 0.011321522 0.01642666797517562 0 +8 0 -4.810614 0.008077087 0.011700088369940806 0 +9 0 -3.9204557 0.019446393 0.028331590677166323 0 +10 0 -5.141514 0.005814819 0.0084134959784060125 0 +11 0 -4.9192867 0.0072513726 0.010499633786939466 0 +12 1 -0.85985374 0.2973699 1.7496694835410176 0 +13 0 -4.3980947 0.012151285 0.017637979128819783 0 +14 1 5.6814957 0.99660313 0.0049089879584418182 1 +15 1 0.25190878 0.56264627 0.82969989583644721 1 +16 0 -4.1038265 0.016241249 0.023623530310215374 0 +17 0 -3.8839555 0.020154733 0.029374152252344635 0 +18 1 5.449606 0.9957204 0.0061874265268757394 1 +19 0 -3.0684948 0.04442568 0.065560013263418176 0 +20 1 3.9626808 0.9813426 0.027171185979008489 1 +21 1 5.2650557 0.9948575 0.0074382155674881083 1 +22 0 -4.511556 0.010862078 0.015756394824142473 0 +23 1 ? ? ? 0 +24 0 -5.107146 0.0060169133 0.0087067913827365544 0 +25 1 0.67267895 0.66210276 0.59487295303894816 1 +26 0 -4.3625536 0.012585388 0.018272099665311091 0 +27 0 -3.6960962 0.024219107 0.035370860931637681 0 +28 0 -4.9192867 0.0072513726 0.010499633786939466 0 +29 0 -5.0873837 0.0061362656 0.0088800329967917858 0 +30 0 -4.545924 0.010498964 0.015226877455174592 0 +31 0 -4.6994157 0.009018519 0.013069997580773615 0 +32 1 4.570427 0.98975253 0.014860243285211676 1 +33 0 -4.5300207 0.010665474 0.015469670227781259 0 +34 0 -4.281804 0.013629384 0.019798272915656116 0 +35 0 -4.9192867 0.0072513726 0.010499633786939466 0 +36 1 6.09622 0.9977537 0.0032444004285129453 1 +37 0 -2.040009 0.11506581 0.17635792976232192 0 +38 1 3.6194649 0.97390234 0.038150977482211336 1 +39 1 0.67302036 0.6621791 0.59470659134453041 1 +40 0 ? ? ? 0 +41 1 1.7537603 0.85242647 0.23035270287832674 1 +42 1 5.6079683 0.99634486 0.0052829068123115145 1 +43 1 -0.51844835 0.37321514 1.4219205867507978 0 +44 1 5.661515 0.9965348 0.0050078732952072418 1 +45 0 -5.2448807 0.005246777 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-4.971937 0.0068820207 0.009962979363563133 0 +690 0 -5.2080975 0.005442294 0.0078730134260600656 0 +691 1 3.1485786 0.9588527 0.060618878463704597 1 +692 0 -4.731427 0.008736877 0.0126600342550751 0 +693 0 -4.4294205 0.011780951 0.01709722956474144 0 +694 0 -4.3425336 0.012836619 0.018639216844451963 0 +695 0 -5.1391573 0.005828458 0.0084332877609673198 0 +696 1 4.210288 0.985375 0.02125524431312269 1 +697 1 2.564313 0.9285292 0.10698081017479168 1 +698 1 3.2338276 0.96208763 0.055759787818528549 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..ad0a480605 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-out.txt @@ -0,0 +1,98 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 232 | 7 | 0.9707 + negative || 11 | 433 | 0.9752 + ||====================== +Precision || 0.9547 | 0.9841 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.117326 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874384 +AUC: 0.995995 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995995 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.954733 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984091 (0.0000) +Negative recall: 0.975225 (0.0000) +Log-loss: 0.117326 (0.0000) +Log-loss reduction: 0.874384 (0.0000) +F1 Score: 0.962656 (0.0000) +AUPRC: 0.991908 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..853ec2e2eb --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995995 0.973646 0.954733 0.970711 0.984091 0.975225 0.117326 0.874384 0.962656 0.991908 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 AllInstanceSelector WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20;/st:AllInstanceSelector + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..7c7a89aa3d --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-All-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.7285433 0.015308122 0.022255736653645435 0 +1 0 2.2026808 0.91755587 3.6004393879063104 1 +2 0 -2.865943 0.0127788205 0.018554748946255879 0 +3 0 2.3559182 0.93174857 3.8729968952719278 1 +4 0 -2.5510297 0.019316088 0.028139884703690179 0 +5 1 7.342853 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+1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.116559 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875205 +AUC: 0.995920 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995920 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.116559 (0.0000) +Log-loss reduction: 0.875205 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.991714 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..9b3a12e295 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.99592 0.973646 0.958506 0.966527 0.9819 0.977477 0.116559 0.875205 0.9625 0.991714 Average Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Average;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..5025abc412 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Average-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.6282382 0.01783696 0.0259655617646397 0 +1 0 1.967058 0.9275491 3.7868530516332033 1 +2 0 -2.8538728 0.012990052 0.018863469141483385 0 +3 0 1.9359186 0.9245053 3.7274806974608468 1 +4 0 -2.598872 0.018586198 0.027066533968906802 0 +5 1 6.9540396 0.99993664 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b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer-out.txt @@ -0,0 +1,50 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 3 learners for the batch 1 +Beginning training model 1 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 260 instances with missing features during training (over 10 iterations; 26 inst/iter) +Trainer 1 of 3 finished in %Time% +Beginning training model 2 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) +Trainer 2 of 3 finished in %Time% +Beginning training model 3 of 3 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 110 instances with missing features during training (over 10 iterations; 11 inst/iter) +Trainer 3 of 3 finished in %Time% +Training calibrator. +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 || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.112168 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.879907 +AUC: 0.996240 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996240 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.112168 (0.0000) +Log-loss reduction: 0.879907 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.992400 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..5a83bbedba --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.99624 0.973646 0.958506 0.966527 0.9819 0.977477 0.112168 0.879907 0.9625 0.9924 AvgPer 3 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:AvgPer;/nm:3 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..b9559aa981 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-AvgPer-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.4828715 0.022534288 0.032881999799805425 0 +1 0 2.9472694 0.89578646 3.2623854205088092 1 +2 0 -4.2678914 0.011065352 0.016052908381687058 0 +3 0 2.986453 0.89910704 3.3091025690874707 1 +4 0 -3.5019512 0.022150522 0.032315689216100618 0 +5 1 10.2867985 0.999865 0.00019478350072454816 1 +6 0 -2.3605843 0.06085541 0.090580805182194468 0 +7 0 -4.9923854 0.005709156 0.0082601727532068629 0 +8 0 -4.683963 0.007570115 0.010962915067788384 0 +9 0 -4.030381 0.013733364 0.0199503642579353 0 +10 0 -5.6011734 0.0032671653 0.0047212398797933366 0 +11 0 -5.476469 0.0036632805 0.0052947005483042842 0 +12 1 -0.42497015 0.27806875 1.84648646876029 0 +13 0 -5.052911 0.0054013343 0.0078135991288357728 0 +14 1 8.086931 0.9989771 0.0014764523778664522 1 +15 1 1.638258 0.7202816 0.47336704267911461 1 +16 0 -4.3381057 0.010379733 0.015053047889961934 0 +17 0 -4.0520535 0.01346566 0.019558824244149512 0 +18 1 7.524542 0.99828434 0.0024773000217939069 1 +19 0 -2.91369 0.037478272 0.055108986183136477 0 +20 1 6.2183647 0.9943106 0.00823148228811609 1 +21 1 7.010025 0.9972474 0.0039766409692087298 1 +22 0 -4.9072876 0.0061716614 0.0089314146085223197 0 +23 1 ? ? ? 0 +24 0 -5.7595987 0.0028249216 0.0040812677703320503 0 +25 1 1.2282019 0.6383614 0.64755468922941795 1 +26 0 -4.4920645 0.009020099 0.013072298446733502 0 +27 0 -3.7689242 0.017406765 0.025333789038408744 0 +28 0 -5.476469 0.0036632805 0.0052947005483042842 0 +29 0 -5.9523435 0.002366593 0.0034183186220779643 0 +30 0 -4.7488623 0.0071340916 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0.97288966 0.039651900791925578 1 +692 0 -5.19334 0.004749254 0.0068680469780405835 0 +693 0 -4.6390457 0.007887273 0.011424041506677902 0 +694 0 -4.998421 0.0056776917 0.0082145194870921444 0 +695 0 -5.7625217 0.0028173495 0.0040703126069650368 0 +696 1 6.3037586 0.9947386 0.0076106653397057331 1 +697 1 3.8583193 0.95212936 0.070770491793538823 1 +698 1 4.445225 0.97154737 0.041643762631074922 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..f27ae5504a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-out.txt @@ -0,0 +1,141 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 20 of 20 finished in %Time% +List of models and the metrics after sorted +| AUC(Sorted) || Name of Model | +| 0.9969167523124358 |LinearBinaryModelParameters +| 0.9966598150051388 |LinearBinaryModelParameters +| 0.9966598150051388 |LinearBinaryModelParameters +| 0.9966598150051388 |LinearBinaryModelParameters +| 0.9965313463514902 |LinearBinaryModelParameters +| 0.9964028776978417 |LinearBinaryModelParameters +| 0.9964028776978417 |LinearBinaryModelParameters +| 0.9962744090441932 |LinearBinaryModelParameters +| 0.9962744090441932 |LinearBinaryModelParameters +| 0.9961459403905447 |LinearBinaryModelParameters +| 0.9961459403905447 |LinearBinaryModelParameters +| 0.9961459403905447 |LinearBinaryModelParameters +| 0.9958890030832477 |LinearBinaryModelParameters +| 0.9957605344295992 |LinearBinaryModelParameters +| 0.9956320657759506 |LinearBinaryModelParameters +| 0.9955035971223022 |LinearBinaryModelParameters +| 0.9952466598150052 |LinearBinaryModelParameters +| 0.9947327852004111 |LinearBinaryModelParameters +| 0.994218910585817 |LinearBinaryModelParameters +| 0.9939619732785201 |LinearBinaryModelParameters +Warning: 10 of 20 trainings failed. +Training calibrator. +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 || 229 | 10 | 0.9582 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9622 | 0.9775 | +OVERALL 0/1 ACCURACY: 0.972182 +LOG LOSS/instance: 0.117306 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.874405 +AUC: 0.996042 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996042 (0.0000) +Accuracy: 0.972182 (0.0000) +Positive precision: 0.962185 (0.0000) +Positive recall: 0.958159 (0.0000) +Negative precision: 0.977528 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.117306 (0.0000) +Log-loss reduction: 0.874405 (0.0000) +F1 Score: 0.960168 (0.0000) +AUPRC: 0.991960 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..c15ac44dee --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /pt /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996042 0.972182 0.962185 0.958159 0.977528 0.97973 0.117306 0.874405 0.960168 0.99196 BestPerformanceSelector Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /pt:BestPerformanceSelector;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..fa0556c761 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-BestPerf-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.6674228 0.017303217 0.025181762183421101 0 +1 0 1.9218833 0.93519956 3.9478525524547492 1 +2 0 -2.842935 0.013440117 0.019521471775374808 0 +3 0 1.9052122 0.9337069 3.9149970283743163 1 +4 0 -2.6474748 0.017806107 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0000000000..1d14d97e5d --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer-out.txt @@ -0,0 +1,216 @@ +maml.exe CV tr=WeightedEnsemble{nm=20 tp=-} threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 129 | 5 | 0.9627 + negative || 7 | 213 | 0.9682 + ||====================== +Precision || 0.9485 | 0.9771 | +OVERALL 0/1 ACCURACY: 0.966102 +LOG LOSS/instance: 0.143167 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 0.850400 +AUC: 0.993996 +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 || 103 | 2 | 0.9810 + negative || 4 | 220 | 0.9821 + ||====================== +Precision || 0.9626 | 0.9910 | +OVERALL 0/1 ACCURACY: 0.981763 +LOG LOSS/instance: 0.115437 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 0.872227 +AUC: 0.997832 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995914 (0.0019) +Accuracy: 0.973932 (0.0078) +Positive precision: 0.955573 (0.0070) +Positive recall: 0.971819 (0.0091) +Negative precision: 0.984028 (0.0070) +Negative recall: 0.975162 (0.0070) +Log-loss: 0.129302 (0.0139) +Log-loss reduction: 0.861313 (0.0109) +F1 Score: 0.963627 (0.0081) +AUPRC: 0.992159 (0.0031) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..a1a6ed89f7 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995914 0.973932 0.955573 0.971819 0.984028 0.975162 0.129302 0.861313 0.963627 0.992159 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Output% 99 0 0 maml.exe CV tr=WeightedEnsemble{nm=20 tp=-} threads=- dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer.txt new file mode 100644 index 0000000000..37bcbd2d33 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 7.0572076 0.99996483 5.0735774115656486E-05 1 +6 0 -0.5697355 0.32680362 0.57090068597019361 0 +8 0 -3.265584 0.0099199135 0.01438286692339372 0 +9 0 -2.8438797 0.018053178 0.026283197990078042 0 +10 0 -3.4260702 0.007889866 0.01142781187212402 0 +11 0 -3.2730165 0.009815386 0.014230562120318395 0 +18 1 4.1297426 0.9976289 0.0034247967532404057 1 +20 1 3.6110172 0.99501014 0.0072168704068302217 1 +21 1 3.7079864 0.9956574 0.0062787129039152982 1 +25 1 0.85096335 0.78957415 0.34085334376785664 1 +28 0 -3.2730165 0.009815386 0.014230562120318395 0 +31 0 -3.1337807 0.011967557 0.017369679358421687 0 +32 1 3.2218153 0.9912949 0.012613756987139313 1 +35 0 -3.2730165 0.009815386 0.014230562120318395 0 +37 0 -2.1029623 0.050705004 0.07507161729782677 0 +40 0 ? ? ? 0 +41 1 0.42940283 0.6716244 0.57427340338024435 1 +44 1 4.518715 0.9986441 0.0019574580164138182 1 +45 0 -3.4117298 0.0080531 0.011665200864927582 0 +46 1 3.4790392 0.9939724 0.0087222723575821337 1 +48 0 -2.6620014 0.023329994 0.03405690291674212 0 +50 1 0.44066358 0.6751893 0.56663601886697856 1 +51 1 -0.21027565 0.44886693 1.1556402728846316 0 +52 1 2.103271 0.9579125 0.062034208068952469 1 +54 1 3.5159762 0.9942828 0.0082718705836708154 1 +56 1 3.9441147 0.99690485 0.0044722823151831939 1 +60 1 0.9666357 0.815908 0.29352158253040972 1 +63 1 -0.135355 0.4756681 1.0719728110289044 0 +64 0 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0.015110836 0.021966717597868491 0 +684 0 -3.299302 0.0048193554 0.0069696683697878879 0 +686 0 -3.299302 0.0048193554 0.0069696683697878879 0 +687 0 -2.4359035 0.014286715 0.020760024239957379 0 +690 0 -3.05244 0.006581786 0.0095268966538914947 0 +695 0 -3.111528 0.0061089722 0.0088404144622395067 0 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..981dca228a --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.115894 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875917 +AUC: 0.995976 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995976 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.115894 (0.0000) +Log-loss reduction: 0.875917 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.991794 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..d908097f1c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995976 0.973646 0.958506 0.966527 0.9819 0.977477 0.115894 0.875917 0.9625 0.991794 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..2cb9fbac02 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Default-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.6328392 0.017363675 0.02527052278685812 0 +1 0 2.117365 0.93862414 4.0261849397609355 1 +2 0 -2.9316711 0.01141518 0.016563340966362451 0 +3 0 2.0085166 0.9290655 3.8173692357034033 1 +4 0 -2.6474748 0.017011689 0.024753833758538169 0 +5 1 6.850296 0.9999226 0.00011170705633068974 1 +6 0 -1.1659651 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b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer-out.txt @@ -0,0 +1,119 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 20 of 20 finished in %Time% +Warning: 10 of 20 trainings failed. +Training calibrator. +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 || 236 | 3 | 0.9874 + negative || 14 | 430 | 0.9685 + ||====================== +Precision || 0.9440 | 0.9931 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.114893 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.876989 +AUC: 0.996127 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996127 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.944000 (0.0000) +Positive recall: 0.987448 (0.0000) +Negative precision: 0.993072 (0.0000) +Negative recall: 0.968468 (0.0000) +Log-loss: 0.114893 (0.0000) +Log-loss reduction: 0.876989 (0.0000) +F1 Score: 0.965235 (0.0000) +AUPRC: 0.992160 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..a080a632bf --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /pt /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996127 0.97511 0.944 0.987448 0.993072 0.968468 0.114893 0.876989 0.965235 0.99216 BestDiverseSelector Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /pt:BestDiverseSelector;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..06ed59bc53 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Diverse-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -2.0473788 0.017723879 0.025799466859665474 0 +1 0 2.472919 0.9068278 3.4239567458634292 1 +2 0 -2.2322254 0.013759199 0.019988155613655037 0 +3 0 2.197465 0.86900395 2.9324048019558933 1 +4 0 -1.9936024 0.019074872 0.02778507238959756 0 +5 1 7.7100306 0.9999298 0.00010130133779963418 1 +6 0 -0.56720495 0.12399351 0.19098653419954184 0 +7 0 -2.607882 0.008203492 0.011883948460682877 0 +8 0 -2.5962741 0.00833597 0.012076668674975928 0 +9 0 -2.2978225 0.012573904 0.0182553218399173 0 +10 0 -3.0386791 0.004521153 0.0065374344513617118 0 +11 0 -2.9418259 0.005170122 0.0074782575179410758 0 +12 1 0.11766362 0.26853293 1.8968290694778103 1 +13 0 -2.418044 0.010657477 0.015458008276805919 0 +14 1 5.6898084 0.998833 0.0016846073197354307 1 +15 1 1.0684388 0.5795737 0.78693598805700216 1 +16 0 -2.4469051 0.010242218 0.01485258957583176 0 +17 0 -2.2948391 0.012625555 0.01833078821011231 0 +18 1 5.0358553 0.99710566 0.0041817073405913587 1 +19 0 -1.7999183 0.024829188 0.036273150363783424 0 +20 1 4.504013 0.9939521 0.008751773519959434 1 +21 1 5.3201275 0.99804944 0.0028168142040815242 1 +22 0 -2.6943655 0.007280065 0.010541331496867676 0 +23 1 ? ? ? 0 +24 0 -3.03722 0.004530301 0.0065506921049130629 0 +25 1 1.3884318 0.6827258 0.55062185009980469 1 +26 0 -2.6406279 0.00784092 0.011356638406891344 0 +27 0 -2.2074814 0.0142343715 0.020683416610047932 0 +28 0 -2.9418259 0.005170122 0.0074782575179410758 0 +29 0 -3.0194645 0.0046431064 0.0067141859607828848 0 +30 0 -2.7705815 0.0065522613 0.0094840198150651801 0 +31 0 -2.7897596 0.006380806 0.0092350524519540494 0 +32 1 4.499022 0.99391025 0.0088125080175020781 1 +33 0 -2.6119566 0.008157486 0.011817029160998801 0 +34 0 -2.5209303 0.009248927 0.013405470573449966 0 +35 0 -2.9418259 0.005170122 0.0074782575179410758 0 +36 1 5.7223725 0.9988846 0.0016100537520699492 1 +37 0 -0.98325634 0.07350039 0.11013772182720206 0 +38 1 4.0876126 0.9892551 0.015585539758591687 1 +39 1 1.9509537 0.8247887 0.27790354582661531 1 +40 0 ? ? ? 0 +41 1 2.0159984 0.83748746 0.25586050766580748 1 +42 1 5.680565 0.9988179 0.0017063887081673129 1 +43 1 0.76707935 0.47543246 1.0726877022339927 1 +44 1 5.820989 0.99902755 0.0014036310654402073 1 +45 0 -3.0401928 0.004511682 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? ? 0 +165 0 -1.8417928 0.02345664 0.034243992314551289 0 +166 1 5.265355 0.9978953 0.0030396395796851217 1 +167 1 4.6380835 0.9949763 0.0072659592572532905 1 +168 0 -2.5422993 0.008980374 0.013014466066836062 0 +169 0 -3.2840765 0.0032174205 0.0046492399557907299 0 +170 0 -2.8522573 0.0058524134 0.0084680512460020736 0 +171 0 -3.03722 0.004530301 0.0065506921049130629 0 +172 0 -3.1892862 0.0036694317 0.0053036074417511375 0 +173 1 8.899683 0.9999866 1.9348177961999343E-05 1 +174 1 3.872163 0.9855527 0.020995035751295892 1 +175 1 5.511031 0.9985038 0.002160170673398052 1 +176 0 -2.7897596 0.006380806 0.0092350524519540494 0 +177 1 4.1980257 0.99077135 0.013375939190817083 1 +178 0 -2.2948391 0.012625555 0.01833078821011231 0 +179 1 1.6457775 0.75481325 0.40580833922720055 1 +180 0 -3.0386791 0.004521153 0.0065374344513617118 0 +181 0 -3.3413522 0.0029716617 0.0042935843640431078 0 +182 0 -1.7999183 0.024829188 0.036273150363783424 0 +183 1 5.783315 0.9989752 0.0014792069204608016 1 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b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-out.txt @@ -0,0 +1,118 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 130 instances with missing features during training (over 10 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 170 instances with missing features during training (over 10 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 190 instances with missing features during training (over 10 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 232 | 7 | 0.9707 + negative || 9 | 435 | 0.9797 + ||====================== +Precision || 0.9627 | 0.9842 | +OVERALL 0/1 ACCURACY: 0.976574 +LOG LOSS/instance: 0.112829 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.879198 +AUC: 0.996259 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996259 (0.0000) +Accuracy: 0.976574 (0.0000) +Positive precision: 0.962656 (0.0000) +Positive recall: 0.970711 (0.0000) +Negative precision: 0.984163 (0.0000) +Negative recall: 0.979730 (0.0000) +Log-loss: 0.112829 (0.0000) +Log-loss reduction: 0.879198 (0.0000) +F1 Score: 0.966667 (0.0000) +AUPRC: 0.992451 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..1ac6423566 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996259 0.976574 0.962656 0.970711 0.984163 0.97973 0.112829 0.879198 0.966667 0.992451 svm,ap 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:svm,ap;/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..90e103c1e2 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Hetero-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -3.2653842 0.015952274 0.023199807675989368 0 +1 0 2.3943 0.8926791 3.2199969729837079 1 +2 0 -3.5103545 0.012222463 0.017741934441503331 0 +3 0 2.3016274 0.8824897 3.0891406473343408 1 +4 0 -2.964328 0.02209367 0.032231814122084979 0 +5 1 9.490734 0.99995196 6.9310673199979037E-05 1 +6 0 -1.4822237 0.10377876 0.1580731722532151 0 +7 0 -3.8577213 0.008365857 0.012120149713014752 0 +8 0 -3.6269126 0.010764261 0.015613732363990333 0 +9 0 -3.6241956 0.010796209 0.015660326004814831 0 +10 0 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dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 233 | 8 | 0.9668 + negative || 12 | 446 | 0.9738 + ||====================== +Precision || 0.9510 | 0.9824 | +OVERALL 0/1 ACCURACY: 0.971388 +LOG LOSS/instance: 0.130701 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 0.859358 +AUC: 0.994927 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.994927 (0.0000) +Accuracy: 0.971388 (0.0000) +Positive precision: 0.951020 (0.0000) +Positive recall: 0.966805 (0.0000) +Negative precision: 0.982379 (0.0000) +Negative recall: 0.973799 (0.0000) +Log-loss: 0.130701 (0.0000) +Log-loss reduction: 0.859358 (0.0000) +F1 Score: 0.958848 (0.0000) +AUPRC: 0.989450 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomFeature-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomFeature-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..2774ce294c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomFeature-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.994927 0.971388 0.95102 0.966805 0.982379 0.973799 0.130701 0.859358 0.958848 0.98945 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 AllInstanceSelector{fs=RandomFeatureSelector} WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20;/st:AllInstanceSelector{fs=RandomFeatureSelector} + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomFeature-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomFeature-TrainTest-breast-cancer.txt 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-3.818494 0.002868181 0.0041438561512361881 0 +687 0 -2.865121 0.010631126 0.015419583039454196 0 +688 0 -2.9438014 0.009545941 0.013838035728266303 0 +689 0 -3.0072126 0.0087517975 0.012681750193826559 0 +690 0 -3.5071023 0.0044043735 0.0063682020462816225 0 +691 1 3.1353 0.97728115 0.033154425243608135 1 +692 0 -3.1744013 0.0069583924 0.010073928241131102 0 +693 0 -2.9298458 0.009730079 0.01410627645602513 0 +694 0 -2.7452216 0.012523636 0.018181878627427504 0 +695 0 -3.4550056 0.004731709 0.0068426147359647426 0 +696 1 4.0279884 0.9932779 0.0097306717027177518 1 +697 1 2.3217704 0.9332035 0.099736348264798103 1 +698 1 2.8459814 0.96648365 0.049182764441884248 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..7ec08f4a2e --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-out.txt @@ -0,0 +1,58 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 1 instances with missing features during training (over 1 iterations; 1 inst/iter) +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) +Trainer 5 of 5 finished in %Time% +Training calibrator. +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 || 235 | 4 | 0.9833 + negative || 13 | 431 | 0.9707 + ||====================== +Precision || 0.9476 | 0.9908 | +OVERALL 0/1 ACCURACY: 0.975110 +LOG LOSS/instance: 0.126392 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.864677 +AUC: 0.995463 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995463 (0.0000) +Accuracy: 0.975110 (0.0000) +Positive precision: 0.947581 (0.0000) +Positive recall: 0.983264 (0.0000) +Negative precision: 0.990805 (0.0000) +Negative recall: 0.970721 (0.0000) +Log-loss: 0.126392 (0.0000) +Log-loss reduction: 0.864677 (0.0000) +F1 Score: 0.965092 (0.0000) +AUPRC: 0.990717 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..eb73780d40 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bp /nm /st Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995463 0.97511 0.947581 0.983264 0.990805 0.970721 0.126392 0.864677 0.965092 0.990717 Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 5 RandomPartitionSelector WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:5;/st:RandomPartitionSelector + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..1abf1990e6 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-RandomPartition-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1.8894541 0.014635542 0.021270660765179913 0 +1 0 2.3887155 0.9462658 4.218016030721552 1 +2 0 -1.9510738 0.013235805 0.019222726322552175 0 +3 0 2.181253 0.92589223 3.7542314515478239 1 +4 0 -1.6781864 0.020632772 0.030078173772772502 0 +5 1 6.4960194 0.99993646 9.1669665219582821E-05 1 +6 0 -0.47220063 0.13414705 0.20780606234723856 0 +7 0 -2.1600382 0.00940348 0.013630542459781892 0 +8 0 -2.301506 0.0074557993 0.010796744002841982 0 +9 0 -1.8806512 0.014847065 0.021580389633454124 0 +10 0 -2.6728587 0.0040472383 0.0058507783280350037 0 +11 0 -2.3560033 0.006817358 0.0098690474636270696 0 +12 1 0.032711744 0.2631957 1.9257922115409605 1 +13 0 -2.0274122 0.011683749 0.016955331728356806 0 +14 1 5.0282917 0.99928 0.0010391493624186305 1 +15 1 1.0242789 0.6481865 0.62551911064298549 1 +16 0 -2.0743132 0.010820864 0.015696284165498346 0 +17 0 -2.0302992 0.011628724 0.016875010714480908 0 +18 1 4.1668844 0.9970126 0.0043163357233667772 1 +19 0 -1.7486088 0.018405357 0.026800719847939695 0 +20 1 3.578246 0.99212766 0.011402330948237581 1 +21 1 3.8523407 0.9949834 0.0072556746590812663 1 +22 0 -2.2151585 0.008590921 0.012447624039547705 0 +23 1 ? ? ? 0 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-2.5932832 0.004614112 0.0066721613681936425 0 +686 0 -2.5932832 0.004614112 0.0066721613681936425 0 +687 0 -1.9832036 0.0125592025 0.018233841609591416 0 +688 0 -2.1183274 0.010068552 0.014599471241909705 0 +689 0 -2.4694989 0.005656847 0.0081842753063353438 0 +690 0 -2.3103478 0.0073483232 0.01064053259217026 0 +691 1 3.3837285 0.9891714 0.015707588087033722 1 +692 0 -2.2591724 0.007992394 0.011576913076588022 0 +693 0 -2.1095245 0.010214754 0.014812558269353718 0 +694 0 -1.9774822 0.012677137 0.018406159184860077 0 +695 0 -2.4000175 0.006341602 0.0091781309117178744 0 +696 1 4.448574 0.9981234 0.0027098943698025662 1 +697 1 2.1282024 0.9196401 0.12085868275888806 1 +698 1 2.6675482 0.96543676 0.050746340022452187 1 diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..9a540e34cd --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 5 finished in %Time% +The number of instances used for stacking trainer is 213 +Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. +Warning: Skipped 40 instances with missing features during training (over 10 iterations; 4 inst/iter) +Training calibrator. +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 || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.116054 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.875745 +AUC: 0.996023 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996023 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.116054 (0.0000) +Log-loss reduction: 0.875745 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.991901 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..8d5f739920 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996023 0.973646 0.958506 0.966527 0.9819 0.977477 0.116054 0.875745 0.9625 0.991901 Stacking{bp=ap} Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 5 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Stacking{bp=ap};/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..4fe7235eaa --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-StackingAP-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -33.543434 0.018149285 0.026424406997653051 0 +1 0 25.170948 0.92242044 3.6881796354000604 1 +2 0 -36.47195 0.013211864 0.019187724511088928 0 +3 0 26.370367 0.9313616 3.8648405956921912 1 +4 0 -33.691265 0.017861417 0.026001486304229247 0 +5 1 89.0962 0.9999263 0.00010628919351499789 1 +6 0 -13.47886 0.14418003 0.22462075056412739 0 +7 0 -40.72689 0.008309944 0.012038804886596898 0 +8 0 -40.3276 0.008680293 0.012577684464776367 0 +9 0 -36.41984 0.013286894 0.019297423567255171 0 +10 0 -47.27704 0.0040565482 0.0058642644141645205 0 +11 0 -45.30328 0.0050365827 0.0072846131072639908 0 +12 1 -6.1713505 0.2736494 1.8695994305043828 0 +13 0 -39.400467 0.009604504 0.013923341716585068 0 +14 1 62.524532 0.99862736 0.0019816545892991999 1 +15 1 5.4120345 0.5743326 0.80004165413661099 1 +16 0 -38.671673 0.010398853 0.01508092188592417 0 +17 0 -36.859234 0.01266717 0.018391595266706966 0 +18 1 58.828243 0.9979391 0.0029763040405446804 1 +19 0 -30.22763 0.025941627 0.037919862857192142 0 +20 1 43.020527 0.9883596 0.016892097400990191 1 +21 1 52.47835 0.9958612 0.005983456460903841 1 +22 0 -41.98748 0.007240539 0.010483890113775743 0 +23 1 ? ? ? 0 +24 0 -46.80664 0.0042713247 0.00617541701187245 0 +25 1 9.4633255 0.6782496 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test=%Data% tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Warning: Training data does not support shuffling, so ignoring request to shuffle +Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) +Trainer 20 of 20 finished in %Time% +Training calibrator. +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 || 231 | 8 | 0.9665 + negative || 10 | 434 | 0.9775 + ||====================== +Precision || 0.9585 | 0.9819 | +OVERALL 0/1 ACCURACY: 0.973646 +LOG LOSS/instance: 0.129466 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 0.861385 +AUC: 0.992339 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.992339 (0.0000) +Accuracy: 0.973646 (0.0000) +Positive precision: 0.958506 (0.0000) +Positive recall: 0.966527 (0.0000) +Negative precision: 0.981900 (0.0000) +Negative recall: 0.977477 (0.0000) +Log-loss: 0.129466 (0.0000) +Log-loss reduction: 0.861385 (0.0000) +F1 Score: 0.962500 (0.0000) +AUPRC: 0.963675 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..1396d347ab --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsemble +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.992339 0.973646 0.958506 0.966527 0.9819 0.977477 0.129466 0.861385 0.9625 0.963675 Voting Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer] 20 WeightedEnsemble %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=%Output% loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=%Data% out=%Output% seed=1 /oc:Voting;/bp:Microsoft.ML.Runtime.ComponentFactoryUtils+SimpleComponentFactory`1[Microsoft.ML.Trainers.LinearSvmTrainer];/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..b19b509f16 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsemble/win-arm/WE-Voting-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -1 0.012469131 0.018102248502573935 0 +1 0 0.9 0.96949726 5.0349175091769718 1 +2 0 -1 0.012469131 0.018102248502573935 0 +3 0 1 0.9795901 5.6145883434887764 1 +4 0 -1 0.012469131 0.018102248502573935 0 +5 1 1 0.9795901 0.029749874765915209 1 +6 0 -0.9 0.018710213 0.027248849528611944 0 +7 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0.018102248502573935 0 +31 0 -1 0.012469131 0.018102248502573935 0 +32 1 1 0.9795901 0.029749874765915209 1 +33 0 -1 0.012469131 0.018102248502573935 0 +34 0 -1 0.012469131 0.018102248502573935 0 +35 0 -1 0.012469131 0.018102248502573935 0 +36 1 1 0.9795901 0.029749874765915209 1 +37 0 -1 0.012469131 0.018102248502573935 0 +38 1 0.9 0.96949726 0.044691268798002393 1 +39 1 0.8 0.95464456 0.066964414934699193 1 +40 0 ? ? ? 0 +41 1 0.8 0.95464456 0.066964414934699193 1 +42 1 1 0.9795901 0.029749874765915209 1 +43 1 0.3 0.7283054 0.45738455403071537 1 +44 1 1 0.9795901 0.029749874765915209 1 +45 0 -1 0.012469131 0.018102248502573935 0 +46 1 1 0.9795901 0.029749874765915209 1 +47 0 -1 0.012469131 0.018102248502573935 0 +48 0 -1 0.012469131 0.018102248502573935 0 +49 1 1 0.9795901 0.029749874765915209 1 +50 1 0.7 0.9330589 0.099959912702360154 1 +51 1 -0.2 0.25450557 1.974230837373812 0 +52 1 1 0.9795901 0.029749874765915209 1 +53 1 0.9 0.96949726 0.044691268798002393 1 +54 1 1 0.9795901 0.029749874765915209 1 +55 1 1 0.9795901 0.029749874765915209 1 +56 1 1 0.9795901 0.029749874765915209 1 +57 1 0.1 0.54034585 0.88804499702609785 1 +58 1 0.7 0.9330589 0.099959912702360154 1 +59 1 0.4 0.80189735 0.31851052964248283 1 +60 1 0.6 0.9022521 0.14839753396284014 1 +61 0 -1 0.012469131 0.018102248502573935 0 +62 1 1 0.9795901 0.029749874765915209 1 +63 1 0.1 0.54034585 0.88804499702609785 1 +64 0 -1 0.012469131 0.018102248502573935 0 +65 1 0.8 0.95464456 0.066964414934699193 1 +66 0 -1 0.012469131 0.018102248502573935 0 +67 1 1 0.9795901 0.029749874765915209 1 +68 1 1 0.9795901 0.029749874765915209 1 +69 0 -1 0.012469131 0.018102248502573935 0 +70 0 -1 0.012469131 0.018102248502573935 0 +71 1 0.9 0.96949726 0.044691268798002393 1 +72 0 -1 0.012469131 0.018102248502573935 0 +73 1 1 0.9795901 0.029749874765915209 1 +74 1 0.8 0.95464456 0.066964414934699193 1 +75 0 -1 0.012469131 0.018102248502573935 0 +76 0 -1 0.012469131 0.018102248502573935 0 +77 0 -1 0.012469131 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0 -1 0.012469131 0.018102248502573935 0 +148 0 -0.5 0.09020085 0.13638000634685848 0 +149 1 1 0.9795901 0.029749874765915209 1 +150 0 -1 0.012469131 0.018102248502573935 0 +151 1 0.9 0.96949726 0.044691268798002393 1 +152 1 1 0.9795901 0.029749874765915209 1 +153 0 -1 0.012469131 0.018102248502573935 0 +154 0 -1 0.012469131 0.018102248502573935 0 +155 1 0.9 0.96949726 0.044691268798002393 1 +156 0 -1 0.012469131 0.018102248502573935 0 +157 0 -1 0.012469131 0.018102248502573935 0 +158 0 ? ? ? 0 +159 1 1 0.9795901 0.029749874765915209 1 +160 1 1 0.9795901 0.029749874765915209 1 +161 0 -1 0.012469131 0.018102248502573935 0 +162 0 -1 0.012469131 0.018102248502573935 0 +163 0 -1 0.012469131 0.018102248502573935 0 +164 0 ? ? ? 0 +165 0 -1 0.012469131 0.018102248502573935 0 +166 1 1 0.9795901 0.029749874765915209 1 +167 1 0.9 0.96949726 0.044691268798002393 1 +168 0 -1 0.012469131 0.018102248502573935 0 +169 0 -1 0.012469131 0.018102248502573935 0 +170 0 -1 0.012469131 0.018102248502573935 0 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0.96949726 0.044691268798002393 1 +313 0 -1 0.012469131 0.018102248502573935 0 +314 0 -1 0.012469131 0.018102248502573935 0 +315 0 ? ? ? 0 +316 1 0.9 0.96949726 0.044691268798002393 1 +317 1 1 0.9795901 0.029749874765915209 1 +318 0 -1 0.012469131 0.018102248502573935 0 +319 0 0.8 0.95464456 4.462580615509057 1 +320 1 1 0.9795901 0.029749874765915209 1 +321 0 ? ? ? 0 +322 0 -1 0.012469131 0.018102248502573935 0 +323 1 0.9 0.96949726 0.044691268798002393 1 +324 0 -1 0.012469131 0.018102248502573935 0 +325 0 -1 0.012469131 0.018102248502573935 0 +326 1 0.9 0.96949726 0.044691268798002393 1 +327 0 -1 0.012469131 0.018102248502573935 0 +328 1 1 0.9795901 0.029749874765915209 1 +329 1 1 0.9795901 0.029749874765915209 1 +330 1 1 0.9795901 0.029749874765915209 1 +331 0 -1 0.012469131 0.018102248502573935 0 +332 0 -1 0.012469131 0.018102248502573935 0 +333 1 0.9 0.96949726 0.044691268798002393 1 +334 1 1 0.9795901 0.029749874765915209 1 +335 0 -1 0.012469131 0.018102248502573935 0 +336 1 0.9 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0.012469131 0.018102248502573935 0 +696 1 1 0.9795901 0.029749874765915209 1 +697 1 0.9 0.96949726 0.044691268798002393 1 +698 1 1 0.9795901 0.029749874765915209 1 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt new file mode 100644 index 0000000000..c8c52a6463 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 45 | 5 | 0.9000 + 2 || 0 | 3 | 47 | 0.9400 + ||======================== +Precision ||1.0000 |0.9375 |0.9038 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.433321 +Log-loss reduction: 0.605574 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.433321 (0.0000) +Log-loss reduction: 0.605574 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..ac49f32b03 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.433321 0.605574 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt new file mode 100644 index 0000000000..4b60908a66 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Average-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.19958509625104848 0.8190705 0.16951077 0.01141867 0 1 2 +1 0 0 0.22949171958730707 0.79493755 0.19322549 0.011837031 0 1 2 +2 0 0 0.20844157984769851 0.81184846 0.1771891 0.010962385 0 1 2 +3 0 0 0.2315363463785047 0.79331386 0.19423674 0.012449473 0 1 2 +4 0 0 0.19394222266281516 0.8237055 0.16497608 0.011318412 0 1 2 +5 0 0 0.25615477277829379 0.77402216 0.20474723 0.021230659 0 1 2 +6 0 0 0.23071794889050412 0.7939634 0.19154221 0.014494381 0 1 2 +7 0 0 0.21332435396586477 0.80789405 0.17988764 0.012218421 0 1 2 +8 0 0 0.23541836887303985 0.79024017 0.19788687 0.011873004 0 1 2 +9 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 +10 0 0 0.19656631015650861 0.82154685 0.16647743 0.011975767 0 1 2 +11 0 0 0.22154100919058439 0.80128306 0.18575905 0.012957819 0 1 2 +12 0 0 0.20380232774069329 0.8156236 0.1750274 0.009349017 0 1 2 +13 0 0 0.1808222416849333 0.8345837 0.15767018 0.0077461596 0 1 2 +14 0 0 0.16079850921766828 0.8514636 0.13880378 0.00973264 0 1 2 +15 0 0 0.20740288151214764 0.81269217 0.1691007 0.018207159 0 1 2 +16 0 0 0.21909979965662399 0.80324155 0.18003035 0.01672817 0 1 2 +17 0 0 0.2250250868760485 0.7984962 0.1870559 0.014447969 0 1 2 +18 0 0 0.23366391641909409 0.7916278 0.1913674 0.017004812 0 1 2 +19 0 0 0.21538798103516299 0.8062286 0.17876595 0.015005595 0 1 2 +20 0 0 0.23104359040697584 0.79370487 0.19246776 0.013827393 0 1 2 +21 0 0 0.24970905734884308 0.7790274 0.20189607 0.019076502 0 1 2 +22 0 0 0.16525463650896541 0.8476778 0.14351057 0.008811684 0 1 2 +23 0 0 0.33745519484716036 0.71358395 0.2589257 0.027490431 0 1 2 +24 0 0 0.24877482798679529 0.77975553 0.20474195 0.015502517 0 1 2 +25 0 0 0.24794503212625785 0.78040284 0.20624886 0.013348286 0 1 2 +26 0 0 0.28121802414883634 0.75486374 0.22453001 0.02060628 0 1 2 +27 0 0 0.20765755811531678 0.8124852 0.17536946 0.012145374 0 1 2 +28 0 0 0.20539224708657894 0.81432784 0.17415427 0.011517986 0 1 2 +29 0 0 0.23412760766040333 0.79126084 0.19560409 0.01313511 0 1 2 +30 0 0 0.24087635296706686 0.7859388 0.20082402 0.013237218 0 1 2 +31 0 0 0.2712309592171685 0.7624404 0.21805911 0.019500589 0 1 2 +32 0 0 0.15532962919885376 0.8561329 0.13477181 0.009095248 0 1 2 +33 0 0 0.16434959721587869 0.84844536 0.14075178 0.010802942 0 1 2 +34 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 +35 0 0 0.20080315237761032 0.81807345 0.17158608 0.010340452 0 1 2 +36 0 0 0.19233578372186541 0.8250298 0.16419326 0.010777007 0 1 2 +37 0 0 0.20602272708386718 0.8138146 0.17631792 0.009867548 0 1 2 +38 0 0 0.22021810241220668 0.8023438 0.18655506 0.011101251 0 1 2 +39 0 0 0.21344026560531973 0.8078004 0.17997126 0.012228421 0 1 2 +40 0 0 0.21630905795028774 0.8054863 0.18092151 0.013592168 0 1 2 +41 0 0 0.30096606575453844 0.7401029 0.24526386 0.014633313 0 1 2 +42 0 0 0.20810486617361554 0.81212187 0.17694256 0.0109356595 0 1 2 +43 0 0 0.34678419522506876 0.7069579 0.26095027 0.03209188 0 1 2 +44 0 0 0.28372706912227269 0.7529721 0.22306935 0.02395857 0 1 2 +45 0 0 0.25799900398330489 0.772596 0.21248487 0.014919168 0 1 2 +46 0 0 0.1984840939936815 0.8199728 0.16743004 0.012597151 0 1 2 +47 0 0 0.21658237113948361 0.8052662 0.1830964 0.011637499 0 1 2 +48 0 0 0.19645719830870367 0.8216365 0.1663978 0.011965793 0 1 2 +49 0 0 0.21102469023036374 0.8097541 0.17865822 0.011587797 0 1 2 +50 1 1 0.72219582983911745 0.4856846 0.3957767 0.1185387 1 2 0 +51 1 1 0.75046978711973689 0.4721447 0.41692048 0.11093482 1 2 0 +52 1 1 0.76958914038145454 0.46320334 0.44806728 0.0887294 1 2 0 +53 1 1 0.57095107325625116 0.56498784 0.27784678 0.15716541 1 2 0 +54 1 1 0.71182454522463745 0.490748 0.41250286 0.09674921 1 2 0 +55 1 1 0.63432438157018267 0.53029364 0.32892045 0.14078592 1 2 0 +56 1 2 0.82703428884420771 0.4764425 0.4373444 0.08621321 2 1 0 +57 1 1 0.6084864208677675 0.5441739 0.3067269 0.14909923 1 0 2 +58 1 1 0.64905915539487302 0.5225372 0.34067142 0.13679142 1 2 0 +59 1 1 0.63344041453667532 0.5307626 0.31403926 0.15519817 1 2 0 +60 1 1 0.54383148046665453 0.58051974 0.26378644 0.15569383 1 0 2 +61 1 1 0.70897643660265774 0.49214768 0.38476583 0.123086534 1 2 0 +62 1 1 0.53748409573418543 0.58421624 0.22847545 0.18730839 1 0 2 +63 1 1 0.6874510444439752 0.5028562 0.38549486 0.11164905 1 2 0 +64 1 1 0.63955301833531797 0.52752817 0.26105487 0.21141699 1 2 0 +65 1 1 0.69528760745748575 0.49893093 0.36722904 0.13384007 1 2 0 +66 1 1 0.7265596703602144 0.48356977 0.40963522 0.10679509 1 2 0 +67 1 1 0.59033377266304388 0.5541423 0.24519745 0.20066026 1 0 2 +68 1 1 0.64053684151642798 0.5270094 0.38393053 0.089060105 1 2 0 +69 1 1 0.57444151987848757 0.5630192 0.22403803 0.21294275 1 0 2 +70 1 2 0.94941076927349677 0.55784464 0.38696897 0.05518642 2 1 0 +71 1 1 0.62548781460243952 0.5350004 0.29014286 0.17485681 1 2 0 +72 1 1 0.70192272007371137 0.49563143 0.42629296 0.07807569 1 2 0 +73 1 1 0.61417194723606394 0.54108876 0.3095269 0.14938432 1 2 0 +74 1 1 0.64068071477248156 0.5269336 0.3164059 0.15666047 1 2 0 +75 1 1 0.68380796394306786 0.5046915 0.3642716 0.13103694 1 2 0 +76 1 1 0.68309004860794009 0.50505394 0.3913477 0.103598416 1 2 0 +77 1 2 0.88339710333663513 0.5294137 0.41337624 0.0572101 2 1 0 +78 1 1 0.71561893779063213 0.48888943 0.4069018 0.10420883 1 2 0 +79 1 1 0.61660375052510907 0.53977454 0.29744694 0.16277865 1 0 2 +80 1 1 0.56687706299463814 0.5672943 0.22793761 0.20476814 1 0 2 +81 1 1 0.577317162489008 0.5614025 0.26665047 0.171947 1 0 2 +82 1 1 0.60183881197342548 0.5478034 0.24704412 0.2051525 1 2 0 +83 1 2 0.79614745102428552 0.48727614 0.45106336 0.061660558 2 1 0 +84 1 1 0.72636216626290295 0.4836653 0.4093965 0.10693822 1 2 0 +85 1 2 0.82100250307584133 0.46231195 0.43999034 0.09769779 2 1 0 +86 1 1 0.75338201554753248 0.4707717 0.4310279 0.0982004 1 2 0 +87 1 1 0.57888302932035285 0.5605241 0.3083013 0.1311746 1 2 0 +88 1 1 0.64901124808319288 0.5225622 0.30197474 0.17546313 1 2 0 +89 1 1 0.59219632846582471 0.55311114 0.2825799 0.16430902 1 2 0 +90 1 1 0.58805734238203022 0.5554052 0.2812 0.16339482 1 2 0 +91 1 1 0.69310188205590872 0.50002265 0.38028446 0.119692944 1 2 0 +92 1 1 0.58894654035017713 0.55491155 0.25228664 0.19280186 1 2 0 +93 1 1 0.59555693924738862 0.55125546 0.30050763 0.14823692 1 0 2 +94 1 1 0.6163831455564952 0.5398936 0.30269963 0.15740676 1 2 0 +95 1 1 0.63274574343740053 0.53113145 0.27512455 0.19374405 1 2 0 +96 1 1 0.63872120368860863 0.52796715 0.30767828 0.16435464 1 2 0 +97 1 1 0.64060798380199047 0.52697194 0.31617925 0.15684882 1 2 0 +98 1 1 0.62999145933178791 0.53259635 0.31007034 0.15733333 1 0 2 +99 1 1 0.62619752826924102 0.5346208 0.2974563 0.1679229 1 2 0 +100 2 2 0.17855884164654245 0.83647484 0.15768342 0.005841737 2 1 0 +101 2 2 0.51438594072881816 0.5978676 0.36759806 0.03453433 2 1 0 +102 2 2 0.31795684125829116 0.7276342 0.25760728 0.014758566 2 1 0 +103 2 2 0.49823894397488383 0.60759974 0.35935712 0.033043154 2 1 0 +104 2 2 0.28872132256143845 0.74922097 0.23817556 0.012603528 2 1 0 +105 2 2 0.26646888093423426 0.76607984 0.22437564 0.00954458 2 1 0 +106 2 2 0.7589634876280229 0.46815142 0.4639967 0.0678519 2 1 0 +107 2 2 0.41958455434772046 0.65731984 0.32084 0.021840187 2 1 0 +108 2 2 0.50042215400829326 0.60627466 0.36666974 0.027055612 2 1 0 +109 2 2 0.16481111621601985 0.8480539 0.14622973 0.0057165036 2 1 0 +110 2 2 0.42702228566975065 0.652449 0.3166652 0.030885829 2 1 0 +111 2 2 0.4893716166670295 0.6130115 0.3563265 0.030662058 2 1 0 +112 2 2 0.35170031947732111 0.7034909 0.27765292 0.018856196 2 1 0 +113 2 2 0.48204975119406152 0.61751634 0.35384622 0.028637458 2 1 0 +114 2 2 0.27777384893727569 0.7574681 0.23055182 0.011980108 2 1 0 +115 2 2 0.28059316572018905 0.75533557 0.23045404 0.014210398 2 1 0 +116 2 2 0.50347036709304971 0.6044294 0.3599218 0.035648745 2 1 0 +117 2 2 0.20080264235933962 0.81807387 0.17373703 0.008189102 2 1 0 +118 2 2 0.20564253176932465 0.81412405 0.18125726 0.0046187183 2 1 0 +119 2 1 0.86046918063867439 0.5074797 0.4229636 0.06955671 1 2 0 +120 2 2 0.25282379421723178 0.7766047 0.212359 0.011036384 2 1 0 +121 2 2 0.47461467332341556 0.62212473 0.3456202 0.032255124 2 1 0 +122 2 2 0.30347774085861567 0.7382463 0.25091964 0.010834171 2 1 0 +123 2 2 0.60340380489364476 0.54694676 0.40614173 0.04691155 2 1 0 +124 2 2 0.32004828605471586 0.726114 0.25618762 0.01769846 2 1 0 +125 2 2 0.43303698796635431 0.6485365 0.3237896 0.027673949 2 1 0 +126 2 2 0.61077728317965752 0.5429287 0.40642515 0.050646145 2 1 0 +127 2 2 0.58269706409653388 0.5583903 0.39157522 0.05003446 2 1 0 +128 2 2 0.35385003319828712 0.70198023 0.28087547 0.01714433 2 1 0 +129 2 2 0.5879566837223279 0.5554611 0.3998645 0.044674482 2 1 0 +130 2 2 0.39671937822604175 0.6725227 0.30802768 0.01944971 2 1 0 +131 2 2 0.28011342012998608 0.755698 0.22862568 0.015676383 2 1 0 +132 2 2 0.31320891182709099 0.73109716 0.25515965 0.013743271 2 1 0 +133 2 1 0.79123517933853238 0.47133562 0.45328456 0.07537993 1 2 0 +134 2 1 0.80281015513384535 0.48515567 0.44806805 0.06677631 1 2 0 +135 2 2 0.2351670056523546 0.79043883 0.20133244 0.008228722 2 1 0 +136 2 2 0.22168771013435326 0.8011655 0.18912463 0.009709963 2 1 0 +137 2 2 0.49720728351907328 0.6082269 0.35535914 0.036414057 2 1 0 +138 2 2 0.59710757595203889 0.55040133 0.3967017 0.052896984 2 1 0 +139 2 2 0.35526349774542548 0.7009887 0.27861634 0.020395001 2 1 0 +140 2 2 0.23237639390231318 0.7926477 0.19817412 0.009178202 2 1 0 +141 2 2 0.29932569329060954 0.7413179 0.24296947 0.015712652 2 1 0 +142 2 2 0.51438594072881816 0.5978676 0.36759806 0.03453433 2 1 0 +143 2 2 0.2402848337371207 0.78640383 0.20385058 0.009745582 2 1 0 +144 2 2 0.19277744540305095 0.8246655 0.16827968 0.0070549594 2 1 0 +145 2 2 0.29649330978501232 0.7434206 0.2420479 0.014531477 2 1 0 +146 2 2 0.5395821941880945 0.5829918 0.3820818 0.03492641 2 1 0 +147 2 2 0.42796030443702671 0.6518373 0.32015815 0.02800457 2 1 0 +148 2 2 0.26536007806283352 0.76692975 0.21919549 0.013874813 2 1 0 +149 2 2 0.55564480447715969 0.5737022 0.38143846 0.044859335 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt new file mode 100644 index 0000000000..994b20dc27 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-out.txt @@ -0,0 +1,151 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 20 learners for the batch 1 +Beginning training model 1 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 20 finished in %Time% +Beginning training model 2 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 20 finished in %Time% +Beginning training model 3 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 20 finished in %Time% +Beginning training model 4 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 20 finished in %Time% +Beginning training model 5 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 5 of 20 finished in %Time% +Beginning training model 6 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 6 of 20 finished in %Time% +Beginning training model 7 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 7 of 20 finished in %Time% +Beginning training model 8 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 8 of 20 finished in %Time% +Beginning training model 9 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 9 of 20 finished in %Time% +Beginning training model 10 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 10 of 20 finished in %Time% +Beginning training model 11 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 11 of 20 finished in %Time% +Beginning training model 12 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 12 of 20 finished in %Time% +Beginning training model 13 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 13 of 20 finished in %Time% +Beginning training model 14 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 14 of 20 finished in %Time% +Beginning training model 15 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 15 of 20 finished in %Time% +Beginning training model 16 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 16 of 20 finished in %Time% +Beginning training model 17 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 17 of 20 finished in %Time% +Beginning training model 18 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 18 of 20 finished in %Time% +Beginning training model 19 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 19 of 20 finished in %Time% +Beginning training model 20 of 20 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 20 of 20 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 46 | 4 | 0.9200 + 2 || 0 | 4 | 46 | 0.9200 + ||======================== +Precision ||1.0000 |0.9200 |0.9200 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.435234 +Log-loss reduction: 0.603833 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.435234 (0.0000) +Log-loss reduction: 0.603833 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..b3236f6187 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.435234 0.603833 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt new file mode 100644 index 0000000000..fb2167d15d --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Bootstrap-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.19132521950387102 0.82586396 0.16326165 0.011272334 0 1 2 +1 0 0 0.21058122478131439 0.81011325 0.17777567 0.011607351 0 1 2 +2 0 0 0.19377139152550549 0.8238462 0.1655918 0.010643643 0 1 2 +3 0 0 0.21394833989369735 0.8073901 0.18037504 0.012178002 0 1 2 +4 0 0 0.18749378711852813 0.82903427 0.1599744 0.011120496 0 1 2 +5 0 0 0.25456562337805144 0.7752532 0.20198968 0.021105848 0 1 2 +6 0 0 0.2199577562989645 0.8025527 0.18342122 0.013983405 0 1 2 +7 0 0 0.20268324355207298 0.81653684 0.17203337 0.012051117 0 1 2 +8 0 0 0.21455993600206141 0.80689645 0.18077749 0.011551563 0 1 2 +9 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 +10 0 0 0.19181567954629949 0.825459 0.16280192 0.011902858 0 1 2 +11 0 0 0.21056930558990042 0.8101229 0.17773885 0.012758404 0 1 2 +12 0 0 0.1857633708736893 0.8304701 0.16012534 0.009209672 0 1 2 +13 0 0 0.16367716426818307 0.8490161 0.14334759 0.007550855 0 1 2 +14 0 0 0.1592486638167529 0.8527843 0.1366698 0.009606908 0 1 2 +15 0 0 0.20768748980943236 0.8124609 0.16827278 0.017978072 0 1 2 +16 0 0 0.21681237325434061 0.805081 0.17695686 0.016417108 0 1 2 +17 0 0 0.21684939173278467 0.8050512 0.181056 0.014152357 0 1 2 +18 0 0 0.23136175078772675 0.7934524 0.18847735 0.01705836 0 1 2 +19 0 0 0.21197144694890696 0.8089878 0.175268 0.014743009 0 1 2 +20 0 0 0.22015815366279803 0.8023919 0.18439388 0.013820611 0 1 2 +21 0 0 0.24543394080765951 0.78236496 0.19777209 0.018589469 0 1 2 +22 0 0 0.15849039000781612 0.85343117 0.13754007 0.008510644 0 1 2 +23 0 0 0.323425302027813 0.723666 0.24987355 0.026793782 0 1 2 +24 0 0 0.23711069983685371 0.78890395 0.19630879 0.015339675 0 1 2 +25 0 0 0.22815863427568761 0.795998 0.19058642 0.013238922 0 1 2 +26 0 0 0.27027251016522602 0.7631715 0.21731177 0.020077024 0 1 2 +27 0 0 0.1992888890650909 0.81931317 0.16926673 0.012075843 0 1 2 +28 0 0 0.19507242562593269 0.82277507 0.16646528 0.011425154 0 1 2 +29 0 0 0.21828009480014346 0.80390024 0.18383104 0.012923741 0 1 2 +30 0 0 0.22282477688327434 0.80025506 0.18709216 0.013062084 0 1 2 +31 0 0 0.26074300389946803 0.7704789 0.21109545 0.019286428 0 1 2 +32 0 0 0.15387950553049615 0.8573753 0.13263428 0.0090501495 0 1 2 +33 0 0 0.16340172022875207 0.84924996 0.13898855 0.010684636 0 1 2 +34 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 +35 0 0 0.186637363615415 0.8297446 0.16040832 0.010106187 0 1 2 +36 0 0 0.18440564571580689 0.8315984 0.1581842 0.010682614 0 1 2 +37 0 0 0.18901217067275977 0.82777643 0.16303392 0.009775385 0 1 2 +38 0 0 0.20158721223460938 0.8174323 0.17107035 0.010779634 0 1 2 +39 0 0 0.20284662392296379 0.81640345 0.17213078 0.0121171195 0 1 2 +40 0 0 0.2082159904393574 0.8120316 0.17468612 0.01320599 0 1 2 +41 0 0 0.27327101245786778 0.76088655 0.22326237 0.014362189 0 1 2 +42 0 0 0.19324670783718506 0.8242786 0.16498175 0.010594476 0 1 2 +43 0 0 0.3385856409638423 0.71277773 0.25433904 0.030721603 0 1 2 +44 0 0 0.28146251667171029 0.7546792 0.21964729 0.023636864 0 1 2 +45 0 0 0.2383924590629562 0.7878934 0.19639453 0.014524944 0 1 2 +46 0 0 0.1947321443118433 0.8230551 0.16418305 0.012469268 0 1 2 +47 0 0 0.20147857192484991 0.8175211 0.1713196 0.011324062 0 1 2 +48 0 0 0.19160297775405896 0.8256346 0.16265854 0.011878425 0 1 2 +49 0 0 0.19840900700849953 0.8200344 0.16919632 0.011390395 0 1 2 +50 1 1 0.69322776928686591 0.4999597 0.3853095 0.11845152 1 2 0 +51 1 1 0.722523737942113 0.48552537 0.40674594 0.11110522 1 2 0 +52 1 1 0.74118472896497323 0.476549 0.43797347 0.08917824 1 2 0 +53 1 1 0.58836871864669449 0.5552323 0.27684516 0.16245264 1 2 0 +54 1 1 0.69428353286528466 0.49943215 0.40868056 0.09834443 1 2 0 +55 1 1 0.63362808545632565 0.530663 0.32093644 0.14328434 1 2 0 +56 1 2 0.79959616587148041 0.4630551 0.44951046 0.08706953 2 1 0 +57 1 1 0.64440951526902035 0.52497244 0.3182307 0.14771065 1 0 2 +58 1 1 0.63849826169205637 0.5280849 0.3335123 0.13852577 1 2 0 +59 1 1 0.64358454344338289 0.5254057 0.3056866 0.15937182 1 2 0 +60 1 1 0.58011570688742153 0.5598336 0.27846277 0.15838164 1 0 2 +61 1 1 0.69450422580740201 0.49932194 0.3758699 0.1251182 1 2 0 +62 1 1 0.55819430407260417 0.5722414 0.23717935 0.18838713 1 0 2 +63 1 1 0.6706556964430801 0.51137316 0.37802836 0.11328237 1 2 0 +64 1 1 0.64363933887463753 0.5253769 0.25090748 0.21586336 1 2 0 +65 1 1 0.67452286668773642 0.5093994 0.3597653 0.13466813 1 2 0 +66 1 1 0.7058186062568671 0.49370426 0.4022218 0.108565226 1 2 0 +67 1 1 0.59242901479605692 0.55298245 0.24969587 0.19693922 1 0 2 +68 1 1 0.65039829460349152 0.5218379 0.3823877 0.091813944 1 2 0 +69 1 1 0.58444694919906981 0.55741405 0.23062757 0.2096751 1 0 2 +70 1 2 0.93080874331150321 0.5452608 0.39423475 0.057120673 2 1 0 +71 1 1 0.63102472941869114 0.5320463 0.28162932 0.17826994 1 2 0 +72 1 1 0.69033612711200742 0.5014075 0.42124325 0.07964288 1 2 0 +73 1 1 0.61287811089741484 0.5417893 0.29978806 0.15209843 1 2 0 +74 1 1 0.63756560507914373 0.5285776 0.30946577 0.1597281 1 2 0 +75 1 1 0.66787150798673112 0.5127989 0.35810578 0.13226116 1 2 0 +76 1 1 0.66578728200726467 0.5138688 0.38506472 0.10513146 1 2 0 +77 1 2 0.86445355892134246 0.52008235 0.4212817 0.058823362 2 1 0 +78 1 1 0.69788533425639254 0.49763653 0.40152365 0.10593243 1 2 0 +79 1 1 0.63952793519090723 0.5275414 0.3048635 0.15907879 1 0 2 +80 1 1 0.58304799218320591 0.5581944 0.23560756 0.20275119 1 0 2 +81 1 1 0.60132885651820822 0.5480828 0.27563834 0.17021754 1 0 2 +82 1 1 0.6076095608439529 0.54465127 0.24010527 0.2093268 1 2 0 +83 1 2 0.7735266813825864 0.48145607 0.46138304 0.0626941 2 1 0 +84 1 1 0.70632320155959749 0.4934552 0.40143895 0.108908534 1 2 0 +85 1 1 0.79304223717229416 0.4524662 0.44797558 0.09836484 1 2 0 +86 1 1 0.72624608533242963 0.48372144 0.4217906 0.0986271 1 2 0 +87 1 1 0.59391478425048649 0.55216146 0.30599856 0.13498 1 2 0 +88 1 1 0.64936832681337731 0.52237564 0.2911275 0.17888878 1 2 0 +89 1 1 0.60544091582515225 0.5458337 0.27816093 0.1686275 1 2 0 +90 1 1 0.59586103373270982 0.55108786 0.2754709 0.16703652 1 2 0 +91 1 1 0.67433941239261297 0.5094929 0.3731714 0.12066449 1 2 0 +92 1 1 0.59699398298302908 0.55046386 0.24674685 0.19758156 1 2 0 +93 1 1 0.6326173696846743 0.53119963 0.31314445 0.1478188 1 0 2 +94 1 1 0.62455997313573031 0.535497 0.29504424 0.16068748 1 2 0 +95 1 1 0.63113833333037295 0.5319859 0.2650082 0.19729102 1 2 0 +96 1 1 0.64071216152679444 0.52691704 0.29850063 0.16781297 1 2 0 +97 1 1 0.63829782565271476 0.52819073 0.3087668 0.15995559 1 2 0 +98 1 1 0.66513051932265554 0.5142064 0.32095367 0.15499938 1 0 2 +99 1 1 0.63214901258249123 0.5314485 0.28859413 0.17117685 1 2 0 +100 2 2 0.19829039123696909 0.82013166 0.17368075 0.006389917 2 1 0 +101 2 2 0.52492001288308943 0.5916027 0.37322634 0.035957552 2 1 0 +102 2 2 0.34124428734782847 0.7108852 0.27510756 0.015381213 2 1 0 +103 2 2 0.52038582693501734 0.5942912 0.37211952 0.03392502 2 1 0 +104 2 2 0.30961828372966127 0.733727 0.25341025 0.013335938 2 1 0 +105 2 2 0.29208628803672415 0.7467041 0.24397701 0.009932667 2 1 0 +106 2 1 0.76577599794446771 0.4684857 0.46497297 0.07058331 1 2 0 +107 2 2 0.44339814845170228 0.6418516 0.33686876 0.022488745 2 1 0 +108 2 2 0.51468237905887937 0.5976904 0.37427962 0.027697844 2 1 0 +109 2 2 0.18356797495881952 0.8322953 0.16195424 0.006078326 2 1 0 +110 2 2 0.44805466005579869 0.63886976 0.32866448 0.032294497 2 1 0 +111 2 2 0.50296313148218141 0.6047361 0.36404082 0.03169162 2 1 0 +112 2 2 0.37169361924495159 0.6895655 0.29125726 0.019662026 2 1 0 +113 2 2 0.48947196565210127 0.61294997 0.35768923 0.030139323 2 1 0 +114 2 2 0.29443890179017906 0.74494946 0.24116975 0.01309623 2 1 0 +115 2 2 0.30003035762074348 0.74079573 0.24472979 0.015172614 2 1 0 +116 2 2 0.5246405682688754 0.591768 0.37266344 0.03684561 2 1 0 +117 2 2 0.22365514961512398 0.7995908 0.19136834 0.008522773 2 1 0 +118 2 2 0.22498141984736153 0.79853106 0.1968635 0.004870262 2 1 0 +119 2 1 0.87256895979850835 0.50807655 0.41787666 0.07132143 1 2 0 +120 2 2 0.27434353427838082 0.7600709 0.22864956 0.011649117 2 1 0 +121 2 2 0.48598859809562739 0.6150888 0.35160384 0.03393219 2 1 0 +122 2 2 0.32670277559944516 0.7212981 0.2681096 0.011162259 2 1 0 +123 2 2 0.61058671707920931 0.54303217 0.40973118 0.048139505 2 1 0 +124 2 2 0.34411550788371209 0.70884705 0.27372396 0.018579002 2 1 0 +125 2 2 0.46167099915057769 0.63022965 0.34244224 0.028386518 2 1 0 +126 2 2 0.62005739926164516 0.53791356 0.41044196 0.051944546 2 1 0 +127 2 2 0.59932614435520237 0.5491816 0.39776433 0.051722012 2 1 0 +128 2 2 0.37197691661904925 0.68937016 0.29309052 0.018012952 2 1 0 +129 2 2 0.60909385265637528 0.54384345 0.4126827 0.04537483 2 1 0 +130 2 2 0.41861630699134578 0.6579566 0.32285875 0.019989468 2 1 0 +131 2 2 0.30629397408299347 0.7361702 0.2482419 0.016009163 2 1 0 +132 2 2 0.33107463281607818 0.71815157 0.2677455 0.014577402 2 1 0 +133 2 1 0.80818037023025491 0.4831257 0.44566828 0.07656072 1 2 0 +134 2 1 0.81851974847985365 0.49172622 0.4410841 0.067769825 1 2 0 +135 2 2 0.25768043130959473 0.77284217 0.2194002 0.008686025 2 1 0 +136 2 2 0.24198368123956718 0.785069 0.20458665 0.010457919 2 1 0 +137 2 2 0.52012148333493535 0.5944483 0.36930636 0.037684277 2 1 0 +138 2 2 0.6132729206291232 0.54157543 0.40236047 0.054682706 2 1 0 +139 2 2 0.37573254044009124 0.686786 0.2925374 0.021319237 2 1 0 +140 2 2 0.25172863009263502 0.7774557 0.21311787 0.009873483 2 1 0 +141 2 2 0.31914811293769146 0.7267679 0.25586408 0.01660954 2 1 0 +142 2 2 0.52492001288308943 0.5916027 0.37322634 0.035957552 2 1 0 +143 2 2 0.26242306021753375 0.76918554 0.22122541 0.0103192665 2 1 0 +144 2 2 0.21253605386436611 0.80853117 0.18426085 0.007625526 2 1 0 +145 2 2 0.31396471424900557 0.7305448 0.25457227 0.015475819 2 1 0 +146 2 2 0.54641286647688758 0.5790231 0.3852587 0.036196552 2 1 0 +147 2 2 0.44528589579378047 0.6406411 0.3310116 0.029093679 2 1 0 +148 2 2 0.28667978636463437 0.7507521 0.23463753 0.014798222 2 1 0 +149 2 2 0.57323357655032769 0.5636997 0.38910908 0.04633835 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-out.txt new file mode 100644 index 0000000000..9ea962b19c --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-out.txt @@ -0,0 +1,71 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 10563. +Auto-tuning parameters: L2 = 2.6670152E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 958. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 8928. +Auto-tuning parameters: L2 = 2.6668373E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 874. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 9201. +Auto-tuning parameters: L2 = 2.6673779E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 754. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 10344. +Auto-tuning parameters: L2 = 2.66688E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 976. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Using 1 thread to train. +Automatically choosing a check frequency of 1. +Auto-tuning parameters: maxIterations = 9315. +Auto-tuning parameters: L2 = 2.6674597E-05. +Auto-tuning parameters: L1Threshold (L1/L2) = 0. +Using best model from iteration 1058. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 48 | 2 | 0.9600 + 2 || 0 | 1 | 49 | 0.9800 + ||======================== +Precision ||1.0000 |0.9796 |0.9608 | +Accuracy(micro-avg): 0.980000 +Accuracy(macro-avg): 0.980000 +Log-loss: 0.061647 +Log-loss reduction: 0.943887 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.980000 (0.0000) +Accuracy(macro-avg): 0.980000 (0.0000) +Log-loss: 0.061647 (0.0000) +Log-loss reduction: 0.943887 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..0992b489d5 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.98 0.98 0.061647 0.943887 MultiAverage SDCAMC{nt=1} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:SDCAMC{nt=1};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris.txt new file mode 100644 index 0000000000..dda96255a4 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-SDCA-Average-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.0021632449196932351 0.9978391 0.00216095 6.0477488E-15 0 1 2 +1 0 0 0.0079895884500299184 0.99204224 0.007957783 9.688731E-14 0 1 2 +2 0 0 0.0034284286585717972 0.99657744 0.0034225024 2.3619268E-14 0 1 2 +3 0 0 0.005962289210830086 0.99405545 0.0059445575 1.11924806E-13 0 1 2 +4 0 0 0.0015346853281102198 0.9984665 0.0015335203 3.8285233E-15 0 1 2 +5 0 0 0.0035573859226362497 0.99644893 0.00355117 5.7437474E-14 0 1 2 +6 0 0 0.0034875220381507346 0.99651855 0.003481549 6.5336374E-14 0 1 2 +7 0 0 0.00316841143066658 0.9968366 0.0031634707 1.7388085E-14 0 1 2 +8 0 0 0.0080876483364587699 0.99194497 0.008055029 2.330598E-13 0 1 2 +9 0 0 0.0044032577016354458 0.9956064 0.004393643 2.0090022E-14 0 1 2 +10 0 0 0.0017300303383470188 0.99827147 0.0017285355 2.8492454E-15 0 1 2 +11 0 0 0.0033182659118411637 0.99668723 0.0033128408 3.1852342E-14 0 1 2 +12 0 0 0.0047188101464650705 0.9952923 0.0047077425 2.2786569E-14 0 1 2 +13 0 0 0.0022097785731952629 0.99779266 0.0022074247 7.5620755E-15 0 1 2 +14 0 0 0.00061662130811644331 0.99938357 0.0006164594 1.3059722E-16 0 1 2 +15 0 0 0.00080075129044645252 0.99919957 0.00080043694 1.7789085E-15 0 1 2 +16 0 0 0.0019398060233437617 0.9980621 0.0019379379 9.545191E-15 0 1 2 +17 0 0 0.0035064230869142686 0.9964997 0.003500329 2.8169651E-14 0 1 2 +18 0 0 0.0035088754643611184 0.9964973 0.0035027952 1.6867416E-14 0 1 2 +19 0 0 0.0017625716470783322 0.998239 0.0017610815 9.636709E-15 0 1 2 +20 0 0 0.0055482836599041047 0.9944671 0.0055329977 3.4685006E-14 0 1 2 +21 0 0 0.0038022471215707378 0.996205 0.0037950575 7.5571704E-14 0 1 2 +22 0 0 0.00065187000241988691 0.99934834 0.0006516751 7.7056546E-16 0 1 2 +23 0 0 0.025671561453119392 0.97465515 0.025344927 6.8817994E-12 0 1 2 +24 0 0 0.0054063646497523711 0.9946082 0.005391832 1.3369E-13 0 1 2 +25 0 0 0.01166462650510286 0.98840314 0.011596839 2.3580305E-13 0 1 2 +26 0 0 0.0096848780603521201 0.99036187 0.009638128 5.946571E-13 0 1 2 +27 0 0 0.0026899363724517031 0.9973137 0.0026863029 9.070409E-15 0 1 2 +28 0 0 0.0030583968972770301 0.9969463 0.0030537776 9.592563E-15 0 1 2 +29 0 0 0.0055600911742843106 0.99445534 0.005544726 9.881548E-14 0 1 2 +30 0 0 0.0078139218642764643 0.9922165 0.0077834995 1.5758193E-13 0 1 2 +31 0 0 0.010642996060550395 0.98941344 0.010586558 2.912449E-13 0 1 2 +32 0 0 0.00030426839186627181 0.9996958 0.00030433448 8.896805E-17 0 1 2 +33 0 0 0.00038851893370880301 0.99961156 0.00038838325 1.396318E-16 0 1 2 +34 0 0 0.0044032577016354458 0.9956064 0.004393643 2.0090022E-14 0 1 2 +35 0 0 0.0034938622235497398 0.99651223 0.0034878587 1.2164258E-14 0 1 2 +36 0 0 0.0023991612209818066 0.9976037 0.0023962657 3.0016866E-15 0 1 2 +37 0 0 0.0044032577016354458 0.9956064 0.004393643 2.0090022E-14 0 1 2 +38 0 0 0.0051597923110851384 0.9948535 0.0051465556 8.414316E-14 0 1 2 +39 0 0 0.0033633582075250873 0.9966423 0.0033576742 1.631592E-14 0 1 2 +40 0 0 0.0028259114314285834 0.9971781 0.0028219246 1.8852812E-14 0 1 2 +41 0 0 0.063285352112480903 0.9386756 0.061324418 1.5070037E-11 0 1 2 +42 0 0 0.002919819727088034 0.99708444 0.0029155666 2.8948588E-14 0 1 2 +43 0 0 0.019364377670876787 0.9808219 0.019178128 7.757467E-12 0 1 2 +44 0 0 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1.3530345898730908E-05 0.99998647 1.3608502E-05 1.9168672E-11 2 1 0 +101 2 2 0.018773745222579363 0.9814014 0.018598154 4.8973E-07 2 1 0 +102 2 2 0.0017586905179432194 0.99824286 0.0017571639 5.8177014E-09 2 1 0 +103 2 2 0.037805546760874928 0.96290016 0.037098948 8.7736424E-07 2 1 0 +104 2 2 0.0003672916597854794 0.9996328 0.00036719942 1.2844347E-09 2 1 0 +105 2 2 0.00044087351856321493 0.9995592 0.00044077556 3.4348288E-10 2 1 0 +106 2 2 0.19962934204546748 0.8190343 0.1809404 2.5332154E-05 2 1 0 +107 2 2 0.018685077196642596 0.9814884 0.018511487 7.663433E-08 2 1 0 +108 2 2 0.018027053928997613 0.98213446 0.017865473 7.345075E-08 2 1 0 +109 2 2 6.8249647222011489E-05 0.99993175 6.832264E-05 1.0774244E-10 2 1 0 +110 2 2 0.044449508434081082 0.9565239 0.043474182 1.9678187E-06 2 1 0 +111 2 2 0.020430427852599752 0.97977686 0.02022291 2.9133497E-07 2 1 0 +112 2 2 0.0040348400172863768 0.9959733 0.0040266765 2.983499E-08 2 1 0 +113 2 2 0.0048605717111177022 0.9951512 0.0048487517 5.7892255E-08 2 1 0 +114 2 2 0.0001059230634154853 0.9998941 0.00010597568 3.3340594E-10 2 1 0 +115 2 2 0.00090628194074056107 0.9990941 0.0009059074 7.2340485E-09 2 1 0 +116 2 2 0.071237519718544848 0.9312407 0.068756916 2.4295111E-06 2 1 0 +117 2 2 0.00072457808662163502 0.9992757 0.000724276 2.5542801E-09 2 1 0 +118 2 2 1.0311656711091902E-05 0.9999897 1.0362112E-05 6.1216575E-13 2 1 0 +119 2 2 0.45803926042444582 0.63252264 0.36746326 1.41449855E-05 2 1 0 +120 2 2 0.00047653344972066648 0.9995236 0.00047653765 1.5639557E-09 2 1 0 +121 2 2 0.01087598057789437 0.98918295 0.010816745 3.6596313E-07 2 1 0 +122 2 2 0.00085074124474289903 0.9991496 0.0008504211 4.515508E-10 2 1 0 +123 2 2 0.15909043594620487 0.8529192 0.14707407 6.8110203E-06 2 1 0 +124 2 2 0.0037816652259080167 0.9962255 0.003774559 4.9691746E-08 2 1 0 +125 2 2 0.061989168824105413 0.93989307 0.060105868 1.0879579E-06 2 1 0 +126 2 2 0.2275894057760163 0.7964512 0.20353334 1.5502652E-05 2 1 0 +127 2 2 0.23148908836684481 0.79335135 0.20662399 2.466404E-05 2 1 0 +128 2 2 0.001149420649647469 0.99885124 0.0011488093 5.3758242E-09 2 1 0 +129 2 2 0.46118083694378575 0.63053864 0.36944872 1.269314E-05 2 1 0 +130 2 2 0.0092526646852335252 0.99079 0.00921002 2.5484363E-08 2 1 0 +131 2 2 0.022876730419094909 0.97738296 0.02261677 2.672307E-07 2 1 0 +132 2 2 0.00037820337633663733 0.99962187 0.00037811566 1.1164417E-09 2 1 0 +133 2 1 1.1971734854547582 0.6978349 0.30204675 0.000118445336 1 2 0 +134 2 2 0.50242590996077208 0.60506105 0.39491317 2.5807594E-05 2 1 0 +135 2 2 0.00027213222801581404 0.9997279 0.00027211176 1.5605003E-10 2 1 0 +136 2 2 0.00018091646128941553 0.9998191 0.0001809948 9.86708E-10 2 1 0 +137 2 2 0.077497879734858929 0.925429 0.07456735 3.684995E-06 2 1 0 +138 2 2 0.27141343897211689 0.76230127 0.23766361 3.5199013E-05 2 1 0 +139 2 2 0.0082592762789907258 0.99177474 0.008225126 8.4806054E-08 2 1 0 +140 2 2 0.00013715969358890206 0.99986285 0.00013718118 2.7744101E-10 2 1 0 +141 2 2 0.0029276507980937399 0.99707663 0.0029233652 1.649818E-08 2 1 0 +142 2 2 0.018773745222579363 0.9814014 0.018598154 4.8973E-07 2 1 0 +143 2 2 0.00021412280668525778 0.9997859 0.00021412154 5.6835286E-10 2 1 0 +144 2 2 5.9427596625705856E-05 0.9999406 5.9394806E-05 1.04773835E-10 2 1 0 +145 2 2 0.0011606392732310788 0.99884003 0.0011599427 5.180256E-09 2 1 0 +146 2 2 0.027995750014108291 0.9723925 0.027607147 3.968195E-07 2 1 0 +147 2 2 0.020005038927645435 0.98019373 0.019805789 4.5477083E-07 2 1 0 +148 2 2 0.00086076339787730691 0.9991396 0.00086038717 1.1015117E-08 2 1 0 +149 2 2 0.10889557677394049 0.89682406 0.10316634 9.609821E-06 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt new file mode 100644 index 0000000000..ef1f8ddb78 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-out.txt @@ -0,0 +1,67 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 5 of 5 finished in %Time% +The number of instances used for stacking trainer is 43 +Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. +Beginning optimization +num vars: 48 +improvement criterion: Mean Improvement +L1 regularization selected 26 of 48 weights. +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 35 | 15 | 0.7000 + 2 || 0 | 0 | 50 | 1.0000 + ||======================== +Precision ||1.0000 |1.0000 |0.7692 | +Accuracy(micro-avg): 0.900000 +Accuracy(macro-avg): 0.900000 +Log-loss: 0.431634 +Log-loss reduction: 0.607110 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.900000 (0.0000) +Accuracy(macro-avg): 0.900000 (0.0000) +Log-loss: 0.431634 (0.0000) +Log-loss reduction: 0.607110 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..0a8055938e --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.9 0.9 0.431634 0.60711 MultiStacking{bp=mlr{t-}} mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiStacking{bp=mlr{t-}};/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt new file mode 100644 index 0000000000..542ce8658d --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Stacking-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.18456843242389695 0.83146304 0.12275226 0.045784786 0 1 2 +1 0 0 0.20214058599237553 0.81698006 0.13414729 0.04887257 0 1 2 +2 0 0 0.18886780960827929 0.82789594 0.12566121 0.0464428 0 1 2 +3 0 0 0.2042374594143592 0.81526875 0.13539809 0.04933318 0 1 2 +4 0 0 0.18129421451257496 0.8341899 0.12062063 0.04518944 0 1 2 +5 0 0 0.22910497108966421 0.79524505 0.14964029 0.055114582 0 1 2 +6 0 0 0.20599247898241843 0.8138392 0.13618557 0.04997521 0 1 2 +7 0 0 0.19339539089320237 0.82415605 0.12838604 0.047457963 0 1 2 +8 0 0 0.20560263135927859 0.81415653 0.13639703 0.049446475 0 1 2 +9 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 +10 0 0 0.18359826846964028 0.8322701 0.12201079 0.045719218 0 1 2 +11 0 0 0.1991312532671079 0.81944233 0.13198687 0.04857088 0 1 2 +12 0 0 0.18431827795758612 0.83167106 0.122932024 0.045396913 0 1 2 +13 0 0 0.16957589938046716 0.8440227 0.11346526 0.042512167 0 1 2 +14 0 0 0.16156841381666529 0.8508083 0.10780533 0.0413863 0 1 2 +15 0 0 0.19535709636238854 0.8225409 0.12864554 0.04881367 0 1 2 +16 0 0 0.20104048481461023 0.8178793 0.13259548 0.04952511 0 1 2 +17 0 0 0.20257448411033235 0.81662565 0.13397184 0.04940251 0 1 2 +18 0 0 0.2108950743623737 0.80985904 0.1388269 0.05131394 0 1 2 +19 0 0 0.19749954306525297 0.8207805 0.1305829 0.048636615 0 1 2 +20 0 0 0.2059590826256649 0.8138664 0.13622543 0.049908135 0 1 2 +21 0 0 0.22258117574077119 0.80045 0.14595445 0.05359562 0 1 2 +22 0 0 0.16275285950798724 0.8498012 0.108762376 0.041436445 0 1 2 +23 0 0 0.29209403096048014 0.7466983 0.18707062 0.06623104 0 1 2 +24 0 0 0.21896408768070941 0.80335057 0.14426717 0.052382372 0 1 2 +25 0 0 0.21536328865125401 0.8062485 0.14238437 0.0513671 0 1 2 +26 0 0 0.24497624859436443 0.7827231 0.1597444 0.057532523 0 1 2 +27 0 0 0.19007467541054762 0.8268974 0.12622653 0.04687606 0 1 2 +28 0 0 0.18795964100158014 0.82864815 0.12495548 0.046396352 0 1 2 +29 0 0 0.20677255717266285 0.8132046 0.13690367 0.049891815 0 1 2 +30 0 0 0.21094225229927066 0.80982083 0.13956985 0.050609313 0 1 2 +31 0 0 0.23705788916645681 0.7889456 0.15500115 0.056053244 0 1 2 +32 0 0 0.15830993666579354 0.8535852 0.10571726 0.04069744 0 1 2 +33 0 0 0.16458939467986103 0.8482419 0.10965086 0.042107303 0 1 2 +34 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 +35 0 0 0.18375848842038933 0.83213675 0.12241189 0.04545127 0 1 2 +36 0 0 0.17973684252969455 0.83549005 0.119678274 0.044831615 0 1 2 +37 0 0 0.18636277637530219 0.82997245 0.12418057 0.045846995 0 1 2 +38 0 0 0.1956789610087275 0.8222762 0.13009433 0.04762942 0 1 2 +39 0 0 0.1934814578219369 0.8240851 0.12843774 0.04747709 0 1 2 +40 0 0 0.1963727608748696 0.8217059 0.13011126 0.0481829 0 1 2 +41 0 0 0.2497403510890685 0.779003 0.16397238 0.057024594 0 1 2 +42 0 0 0.18862701420916578 0.8280953 0.12551515 0.046389602 0 1 2 +43 0 0 0.30319543938694477 0.73845476 0.19266933 0.06887595 0 1 2 +44 0 0 0.25054767014146001 0.7783744 0.16248466 0.059140977 0 1 2 +45 0 0 0.22318220567341671 0.7999691 0.1471228 0.052908104 0 1 2 +46 0 0 0.18541167729254801 0.8307622 0.12309762 0.046140097 0 1 2 +47 0 0 0.19442318111477996 0.8233094 0.12917225 0.04751833 0 1 2 +48 0 0 0.18351913487149632 0.83233595 0.1219628 0.04570115 0 1 2 +49 0 0 0.19122122437689801 0.82594985 0.1270813 0.046968848 0 1 2 +50 1 2 0.83534671767831947 0.4504997 0.43372408 0.115776286 2 1 0 +51 1 2 0.86767360928270354 0.46955615 0.41992733 0.11051658 2 1 0 +52 1 2 0.93160031328057413 0.50976956 0.3939228 0.09630776 2 1 0 +53 1 1 0.65279797692587294 0.52058715 0.32883742 0.15057544 1 2 0 +54 1 2 0.8558829963104696 0.47106034 0.42490783 0.10403175 2 1 0 +55 1 1 0.72444012642221156 0.4845958 0.38031498 0.13508931 1 2 0 +56 1 2 0.98572865744244387 0.53393507 0.37316722 0.09289771 2 1 0 +57 1 1 0.67065511365313524 0.51137346 0.2846646 0.20396191 1 0 2 +58 1 1 0.74477326226470864 0.47484195 0.39399034 0.1311677 1 2 0 +59 1 1 0.70437240819758196 0.49441877 0.35925204 0.1463293 1 2 0 +60 1 1 0.61578638696316768 0.5402159 0.24439119 0.2153929 1 0 2 +61 1 1 0.80743109299301685 0.44600233 0.43339738 0.120600305 1 2 0 +62 1 1 0.60669673129301405 0.5451487 0.24562658 0.20922476 1 2 0 +63 1 1 0.81010570244336411 0.44481105 0.4414372 0.1137517 1 2 0 +64 1 1 0.67750966457140038 0.5078802 0.30291897 0.18920082 1 2 0 +65 1 1 0.78691505229380421 0.45524704 0.41748127 0.12727176 1 2 0 +66 1 2 0.84926655158409137 0.46270454 0.42772853 0.109566994 2 1 0 +67 1 1 0.64189824493287184 0.52629244 0.25330013 0.22040741 1 2 0 +68 1 1 0.79679952257407194 0.45076934 0.44593742 0.10329327 1 2 0 +69 1 1 0.63095605796758758 0.53208286 0.2647365 0.2031807 1 2 0 +70 1 2 1.1786960275307612 0.6199267 0.30767968 0.072393574 2 1 0 +71 1 1 0.6870092540528171 0.5030784 0.33673957 0.16018195 1 2 0 +72 1 2 0.88352349369837391 0.49275747 0.413324 0.09391846 2 1 0 +73 1 1 0.70206728306527211 0.49555978 0.36315414 0.14128618 1 2 0 +74 1 1 0.71553481759306092 0.48893055 0.36550483 0.14556457 1 2 0 +75 1 1 0.77974032878517663 0.45852506 0.41534883 0.12612611 1 2 0 +76 1 2 0.82134976804256588 0.4513136 0.43983757 0.108848765 2 1 0 +77 1 2 1.1169305095604729 0.5973121 0.32728285 0.075404994 2 1 0 +78 1 2 0.84441150267985898 0.46174598 0.42981023 0.10844387 2 1 0 +79 1 1 0.66995205015970571 0.5117331 0.27248898 0.21577786 1 0 2 +80 1 1 0.62519005874700628 0.5351597 0.2573923 0.20744798 1 2 0 +81 1 1 0.63644907348447466 0.5291681 0.24347591 0.22735596 1 0 2 +82 1 1 0.65414397006754765 0.5198869 0.29469877 0.18541431 1 2 0 +83 1 2 1.0172901379110078 0.55721873 0.36157343 0.08120789 2 1 0 +84 1 2 0.84819057006855636 0.46207583 0.428189 0.109735176 2 1 0 +85 1 2 0.95335282800352295 0.5147244 0.38544652 0.09982922 2 1 0 +86 1 2 0.89508022462382064 0.4887798 0.40857482 0.10264545 2 1 0 +87 1 1 0.68518928377675592 0.5039948 0.36442462 0.13158056 1 2 0 +88 1 1 0.70499279569103135 0.49411213 0.34656 0.15932785 1 2 0 +89 1 1 0.66563580799433686 0.51394665 0.33148995 0.15456346 1 2 0 +90 1 1 0.66580375299970151 0.51386034 0.33287135 0.15326837 1 2 0 +91 1 1 0.80308535702097783 0.44794476 0.43358916 0.11846605 1 2 0 +92 1 1 0.64934607696965296 0.52238727 0.3015762 0.17603661 1 2 0 +93 1 1 0.66061179810990611 0.5165352 0.2790302 0.20443459 1 0 2 +94 1 1 0.69199747208392948 0.5005752 0.3514926 0.14793213 1 2 0 +95 1 1 0.68305074992074133 0.5050738 0.32116497 0.17376122 1 2 0 +96 1 1 0.7054437512100572 0.49388936 0.3545214 0.15158923 1 2 0 +97 1 1 0.71484615152695052 0.48926738 0.3649459 0.14578669 1 2 0 +98 1 1 0.68343058388192079 0.504882 0.28716588 0.20795216 1 0 2 +99 1 1 0.69195758365483706 0.50059515 0.34436232 0.15504254 1 2 0 +100 2 2 0.16383717284537735 0.84888023 0.119414344 0.031705547 2 1 0 +101 2 2 0.40337888803804095 0.66805893 0.27109003 0.060850948 2 1 0 +102 2 2 0.24612906771878865 0.7818213 0.17580165 0.04237698 2 1 0 +103 2 2 0.38458455800622765 0.6807334 0.2604145 0.058852218 2 1 0 +104 2 2 0.22841378433724779 0.7957949 0.16403066 0.040174555 2 1 0 +105 2 2 0.20780781252095884 0.81236315 0.15028611 0.037350737 2 1 0 +106 2 2 0.63452694565902779 0.53018624 0.38314953 0.086664274 2 1 0 +107 2 2 0.31204464104261354 0.73194885 0.21806726 0.049983837 2 1 0 +108 2 2 0.37581898482381615 0.6867266 0.25668123 0.05659205 2 1 0 +109 2 2 0.15695450526068122 0.85474294 0.11444273 0.030814389 2 1 0 +110 2 2 0.33815265096555053 0.7130864 0.23231944 0.05459419 2 1 0 +111 2 2 0.3781945273451065 0.6850972 0.2570933 0.057809584 2 1 0 +112 2 2 0.2735254794597754 0.76069295 0.19336773 0.045939334 2 1 0 +113 2 2 0.37479471744171089 0.6874304 0.25543806 0.05713164 2 1 0 +114 2 2 0.22562101511573179 0.7980205 0.16213644 0.039843008 2 1 0 +115 2 2 0.22885563922395138 0.79544336 0.16399483 0.040561795 2 1 0 +116 2 2 0.39164420818511392 0.67594457 0.26396313 0.06009241 2 1 0 +117 2 2 0.17472935308423304 0.83968425 0.12698685 0.033328827 2 1 0 +118 2 2 0.16954447409956053 0.8440492 0.12377211 0.032178555 2 1 0 +119 2 2 0.70613231057368309 0.4935494 0.41598833 0.09046226 2 1 0 +120 2 2 0.20764633396099524 0.81249434 0.14984901 0.037656616 2 1 0 +121 2 2 0.37531422396903868 0.68707335 0.25479668 0.058129955 2 1 0 +122 2 2 0.22775436185945278 0.79631984 0.16398498 0.039695274 2 1 0 +123 2 2 0.48286898939281753 0.61701065 0.31288955 0.07009977 2 1 0 +124 2 2 0.2528409864322595 0.77659136 0.17976019 0.043648347 2 1 0 +125 2 2 0.33013388601944277 0.7188275 0.22825052 0.052921906 2 1 0 +126 2 2 0.49368871104494505 0.61037076 0.31760424 0.07202491 2 1 0 +127 2 2 0.4709665408164333 0.62439847 0.30535403 0.07024766 2 1 0 +128 2 2 0.27231553749172294 0.7616139 0.19291162 0.04547441 2 1 0 +129 2 2 0.456569078421803 0.63345325 0.29930943 0.06723729 2 1 0 +130 2 2 0.29617329757179539 0.74365854 0.20826337 0.048078213 2 1 0 +131 2 2 0.2239003546702904 0.7993948 0.1604567 0.040148415 2 1 0 +132 2 2 0.24435543518708533 0.7832092 0.17473747 0.042053435 2 1 0 +133 2 2 0.65321791839103693 0.5203686 0.38966978 0.089961685 2 1 0 +134 2 2 0.64827650064350351 0.5229463 0.39082098 0.08623271 2 1 0 +135 2 2 0.19240304649373471 0.8249743 0.1395849 0.03544094 2 1 0 +136 2 2 0.19116832885395046 0.82599354 0.13841845 0.03558802 2 1 0 +137 2 2 0.38835812653449592 0.6781694 0.26179168 0.060038935 2 1 0 +138 2 2 0.48601495636537134 0.6150726 0.31270465 0.07222278 2 1 0 +139 2 2 0.2781031392330976 0.7572187 0.19607568 0.04670557 2 1 0 +140 2 2 0.1952137727395046 0.8226588 0.14138144 0.035959862 2 1 0 +141 2 2 0.24156019755662117 0.7854015 0.17241801 0.04218039 2 1 0 +142 2 2 0.40337888803804095 0.66805893 0.27109003 0.060850948 2 1 0 +143 2 2 0.19879031537188224 0.81972176 0.14384045 0.036437757 2 1 0 +144 2 2 0.17283344933093919 0.8412777 0.125729 0.032993343 2 1 0 +145 2 2 0.23808929723711897 0.7881323 0.1702752 0.041592494 2 1 0 +146 2 2 0.42151977767997389 0.656049 0.2816499 0.06230106 2 1 0 +147 2 2 0.334720370279249 0.71553814 0.23087181 0.053590026 2 1 0 +148 2 2 0.220012419647025 0.80250883 0.15794434 0.039546747 2 1 0 +149 2 2 0.44406736163379479 0.6414222 0.2918412 0.06673657 2 1 0 diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt new file mode 100644 index 0000000000..d54a5ba13e --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-out.txt @@ -0,0 +1,61 @@ +maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Training 5 learners for the batch 1 +Beginning training model 1 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 1 of 5 finished in %Time% +Beginning training model 2 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 2 of 5 finished in %Time% +Beginning training model 3 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 11 of 15 weights. +Trainer 3 of 5 finished in %Time% +Beginning training model 4 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 10 of 15 weights. +Trainer 4 of 5 finished in %Time% +Beginning training model 5 of 5 +Beginning optimization +num vars: 15 +improvement criterion: Mean Improvement +L1 regularization selected 12 of 15 weights. +Trainer 5 of 5 finished in %Time% +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 44 | 6 | 0.8800 + 2 || 0 | 2 | 48 | 0.9600 + ||======================== +Precision ||1.0000 |0.9565 |0.8889 | +Accuracy(micro-avg): 0.946667 +Accuracy(macro-avg): 0.946667 +Log-loss: 0.511576 +Log-loss reduction: 0.534344 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.946667 (0.0000) +Accuracy(macro-avg): 0.946667 (0.0000) +Log-loss: 0.511576 (0.0000) +Log-loss reduction: 0.534344 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-rp.txt new file mode 100644 index 0000000000..704a2be9d5 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris-rp.txt @@ -0,0 +1,4 @@ +WeightedEnsembleMulticlass +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.946667 0.946667 0.511576 0.534344 MultiVoting mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiVoting;/bp:mlr{t-};/nm:5 + diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris.txt new file mode 100644 index 0000000000..20d25f54d9 --- /dev/null +++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/win-arm/WE-Voting-TrainTest-iris.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 -0 1 0 0 0 1 2 +1 0 0 -0 1 0 0 0 1 2 +2 0 0 -0 1 0 0 0 1 2 +3 0 0 -0 1 0 0 0 1 2 +4 0 0 -0 1 0 0 0 1 2 +5 0 0 -0 1 0 0 0 1 2 +6 0 0 -0 1 0 0 0 1 2 +7 0 0 -0 1 0 0 0 1 2 +8 0 0 -0 1 0 0 0 1 2 +9 0 0 -0 1 0 0 0 1 2 +10 0 0 -0 1 0 0 0 1 2 +11 0 0 -0 1 0 0 0 1 2 +12 0 0 -0 1 0 0 0 1 2 +13 0 0 -0 1 0 0 0 1 2 +14 0 0 -0 1 0 0 0 1 2 +15 0 0 -0 1 0 0 0 1 2 +16 0 0 -0 1 0 0 0 1 2 +17 0 0 -0 1 0 0 0 1 2 +18 0 0 -0 1 0 0 0 1 2 +19 0 0 -0 1 0 0 0 1 2 +20 0 0 -0 1 0 0 0 1 2 +21 0 0 -0 1 0 0 0 1 2 +22 0 0 -0 1 0 0 0 1 2 +23 0 0 -0 1 0 0 0 1 2 +24 0 0 -0 1 0 0 0 1 2 +25 0 0 -0 1 0 0 0 1 2 +26 0 0 -0 1 0 0 0 1 2 +27 0 0 -0 1 0 0 0 1 2 +28 0 0 -0 1 0 0 0 1 2 +29 0 0 -0 1 0 0 0 1 2 +30 0 0 -0 1 0 0 0 1 2 +31 0 0 -0 1 0 0 0 1 2 +32 0 0 -0 1 0 0 0 1 2 +33 0 0 -0 1 0 0 0 1 2 +34 0 0 -0 1 0 0 0 1 2 +35 0 0 -0 1 0 0 0 1 2 +36 0 0 -0 1 0 0 0 1 2 +37 0 0 -0 1 0 0 0 1 2 +38 0 0 -0 1 0 0 0 1 2 +39 0 0 -0 1 0 0 0 1 2 +40 0 0 -0 1 0 0 0 1 2 +41 0 0 -0 1 0 0 0 1 2 +42 0 0 -0 1 0 0 0 1 2 +43 0 0 -0 1 0 0 0 1 2 +44 0 0 -0 1 0 0 0 1 2 +45 0 0 -0 1 0 0 0 1 2 +46 0 0 -0 1 0 0 0 1 2 +47 0 0 -0 1 0 0 0 1 2 +48 0 0 -0 1 0 0 0 1 2 +49 0 0 -0 1 0 0 0 1 2 +50 1 1 -0 1 0 0 1 0 2 +51 1 1 -0 1 0 0 1 0 2 +52 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +53 1 1 -0 1 0 0 1 0 2 +54 1 1 -0 1 0 0 1 0 2 +55 1 1 -0 1 0 0 1 0 2 +56 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +57 1 1 -0 1 0 0 1 0 2 +58 1 1 -0 1 0 0 1 0 2 +59 1 1 -0 1 0 0 1 0 2 +60 1 1 -0 1 0 0 1 0 2 +61 1 1 -0 1 0 0 1 0 2 +62 1 1 -0 1 0 0 1 0 2 +63 1 1 -0 1 0 0 1 0 2 +64 1 1 -0 1 0 0 1 0 2 +65 1 1 -0 1 0 0 1 0 2 +66 1 1 -0 1 0 0 1 0 2 +67 1 1 -0 1 0 0 1 0 2 +68 1 1 -0 1 0 0 1 0 2 +69 1 1 -0 1 0 0 1 0 2 +70 1 2 34.538776391283193 1 0 0 2 0 1 +71 1 1 -0 1 0 0 1 0 2 +72 1 1 0.22314353641304868 0.8 0.2 0 1 2 0 +73 1 1 -0 1 0 0 1 0 2 +74 1 1 -0 1 0 0 1 0 2 +75 1 1 -0 1 0 0 1 0 2 +76 1 1 -0 1 0 0 1 0 2 +77 1 2 34.538776391283193 1 0 0 2 0 1 +78 1 1 -0 1 0 0 1 0 2 +79 1 1 -0 1 0 0 1 0 2 +80 1 1 -0 1 0 0 1 0 2 +81 1 1 -0 1 0 0 1 0 2 +82 1 1 -0 1 0 0 1 0 2 +83 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +84 1 1 -0 1 0 0 1 0 2 +85 1 2 0.91629071697299402 0.6 0.4 0 2 1 0 +86 1 1 0.22314353641304868 0.8 0.2 0 1 2 0 +87 1 1 -0 1 0 0 1 0 2 +88 1 1 -0 1 0 0 1 0 2 +89 1 1 -0 1 0 0 1 0 2 +90 1 1 -0 1 0 0 1 0 2 +91 1 1 -0 1 0 0 1 0 2 +92 1 1 -0 1 0 0 1 0 2 +93 1 1 -0 1 0 0 1 0 2 +94 1 1 -0 1 0 0 1 0 2 +95 1 1 -0 1 0 0 1 0 2 +96 1 1 -0 1 0 0 1 0 2 +97 1 1 -0 1 0 0 1 0 2 +98 1 1 -0 1 0 0 1 0 2 +99 1 1 -0 1 0 0 1 0 2 +100 2 2 -0 1 0 0 2 0 1 +101 2 2 -0 1 0 0 2 0 1 +102 2 2 -0 1 0 0 2 0 1 +103 2 2 -0 1 0 0 2 0 1 +104 2 2 -0 1 0 0 2 0 1 +105 2 2 -0 1 0 0 2 0 1 +106 2 2 0.51082558402956157 0.6 0.4 0 2 1 0 +107 2 2 -0 1 0 0 2 0 1 +108 2 2 -0 1 0 0 2 0 1 +109 2 2 -0 1 0 0 2 0 1 +110 2 2 -0 1 0 0 2 0 1 +111 2 2 -0 1 0 0 2 0 1 +112 2 2 -0 1 0 0 2 0 1 +113 2 2 -0 1 0 0 2 0 1 +114 2 2 -0 1 0 0 2 0 1 +115 2 2 -0 1 0 0 2 0 1 +116 2 2 -0 1 0 0 2 0 1 +117 2 2 -0 1 0 0 2 0 1 +118 2 2 -0 1 0 0 2 0 1 +119 2 1 1.6094378975329393 0.8 0.2 0 1 2 0 +120 2 2 -0 1 0 0 2 0 1 +121 2 2 -0 1 0 0 2 0 1 +122 2 2 -0 1 0 0 2 0 1 +123 2 2 -0 1 0 0 2 0 1 +124 2 2 -0 1 0 0 2 0 1 +125 2 2 -0 1 0 0 2 0 1 +126 2 2 -0 1 0 0 2 0 1 +127 2 2 -0 1 0 0 2 0 1 +128 2 2 -0 1 0 0 2 0 1 +129 2 2 -0 1 0 0 2 0 1 +130 2 2 -0 1 0 0 2 0 1 +131 2 2 -0 1 0 0 2 0 1 +132 2 2 -0 1 0 0 2 0 1 +133 2 2 0.51082558402956157 0.6 0.4 0 2 1 0 +134 2 1 0.91629071697299402 0.6 0.4 0 1 2 0 +135 2 2 -0 1 0 0 2 0 1 +136 2 2 -0 1 0 0 2 0 1 +137 2 2 -0 1 0 0 2 0 1 +138 2 2 -0 1 0 0 2 0 1 +139 2 2 -0 1 0 0 2 0 1 +140 2 2 -0 1 0 0 2 0 1 +141 2 2 -0 1 0 0 2 0 1 +142 2 2 -0 1 0 0 2 0 1 +143 2 2 -0 1 0 0 2 0 1 +144 2 2 -0 1 0 0 2 0 1 +145 2 2 -0 1 0 0 2 0 1 +146 2 2 -0 1 0 0 2 0 1 +147 2 2 -0 1 0 0 2 0 1 +148 2 2 -0 1 0 0 2 0 1 +149 2 2 -0 1 0 0 2 0 1 diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index 83ffa12321..fb5cc7d123 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// 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. @@ -143,6 +143,15 @@ private IEnumerable GetConfigurationDirs() } + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { + if (RuntimeInformation.ProcessArchitecture == Architecture.Arm64) + configurationDirs.Add("win-arm"); + else if (Environment.Is64BitProcess) + configurationDirs.Add("win-x64"); + else + configurationDirs.Add("win-x86"); + } #if NETCOREAPP // Use netcoreapp result file if necessary. @@ -153,14 +162,6 @@ private IEnumerable GetConfigurationDirs() configurationDirs.Add("netcoreapp"); #endif - if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) - { - if (Environment.Is64BitProcess) - configurationDirs.Add("win-x64"); - else - configurationDirs.Add("win-x86"); - } - return configurationDirs; } From ce9122f44616f05f66e2d0cdc9cd1fb66d3ea150 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Fri, 17 Nov 2023 23:19:38 -0700 Subject: [PATCH 12/17] baseline changes --- .../BaseTestBaseline.cs | 22 +++++++++++-------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index fb5cc7d123..b682639094 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -147,20 +147,24 @@ private IEnumerable GetConfigurationDirs() { if (RuntimeInformation.ProcessArchitecture == Architecture.Arm64) configurationDirs.Add("win-arm"); - else if (Environment.Is64BitProcess) + + // This needs to come after win-arm but before win-x64 and win-x86 +#if NETCOREAPP + // Use netcoreapp result file if necessary. + // The small difference comes from CPUMath using different instruction SSE (in CpuMathNative) vs + // AVX, SSE or direct floating point calculation depending on hardware availability + // (in later versions that use compiler intrinsics). + // There were further differences in floating point calculations introduced in .NET 6.0. + configurationDirs.Add("netcoreapp"); +#endif + + if (Environment.Is64BitProcess) configurationDirs.Add("win-x64"); else configurationDirs.Add("win-x86"); } -#if NETCOREAPP - // Use netcoreapp result file if necessary. - // The small difference comes from CPUMath using different instruction SSE (in CpuMathNative) vs - // AVX, SSE or direct floating point calculation depending on hardware availability - // (in later versions that use compiler intrinsics). - // There were further differences in floating point calculations introduced in .NET 6.0. - configurationDirs.Add("netcoreapp"); -#endif + return configurationDirs; } From f5b825c9d23721428f4851ffb11f4042d1f4bcd8 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Fri, 17 Nov 2023 23:48:33 -0700 Subject: [PATCH 13/17] fixed build issue --- test/Microsoft.ML.TestFramework/BaseTestBaseline.cs | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index b682639094..76f8adf924 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -147,7 +147,7 @@ private IEnumerable GetConfigurationDirs() { if (RuntimeInformation.ProcessArchitecture == Architecture.Arm64) configurationDirs.Add("win-arm"); - + // This needs to come after win-arm but before win-x64 and win-x86 #if NETCOREAPP // Use netcoreapp result file if necessary. @@ -157,7 +157,7 @@ private IEnumerable GetConfigurationDirs() // There were further differences in floating point calculations introduced in .NET 6.0. configurationDirs.Add("netcoreapp"); #endif - + if (Environment.Is64BitProcess) configurationDirs.Add("win-x64"); else From fd1c33a37532160bf62816d34f5ed74edcd20548 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Sat, 18 Nov 2023 00:16:43 -0700 Subject: [PATCH 14/17] fixed test --- .../BaseTestBaseline.cs | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs index 76f8adf924..1c07a60052 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs @@ -147,17 +147,20 @@ private IEnumerable GetConfigurationDirs() { if (RuntimeInformation.ProcessArchitecture == Architecture.Arm64) configurationDirs.Add("win-arm"); + } - // This needs to come after win-arm but before win-x64 and win-x86 + // This needs to come after win-arm but before win-x64 and win-x86 #if NETCOREAPP - // Use netcoreapp result file if necessary. - // The small difference comes from CPUMath using different instruction SSE (in CpuMathNative) vs - // AVX, SSE or direct floating point calculation depending on hardware availability - // (in later versions that use compiler intrinsics). - // There were further differences in floating point calculations introduced in .NET 6.0. - configurationDirs.Add("netcoreapp"); + // Use netcoreapp result file if necessary. + // The small difference comes from CPUMath using different instruction SSE (in CpuMathNative) vs + // AVX, SSE or direct floating point calculation depending on hardware availability + // (in later versions that use compiler intrinsics). + // There were further differences in floating point calculations introduced in .NET 6.0. + configurationDirs.Add("netcoreapp"); #endif + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { if (Environment.Is64BitProcess) configurationDirs.Add("win-x64"); else From c25740b2469e5f9f0ba0adc99fdcdef7dc74f4e3 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Sat, 18 Nov 2023 01:03:26 -0700 Subject: [PATCH 15/17] one more basleine --- .../CommandTrainingLrWithStats-1-out.txt | 1 + .../win-arm/CommandTrainingLrWithStats-out.txt | 17 +++++++++++++++++ .../CommandTrainingLrWithStats-summary.txt | 15 +++++++++++++++ 3 files changed, 33 insertions(+) create mode 100644 test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-1-out.txt create mode 100644 test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-out.txt create mode 100644 test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-summary.txt diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-1-out.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-1-out.txt new file mode 100644 index 0000000000..09df0d1081 --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-1-out.txt @@ -0,0 +1 @@ +Saving predictor summary diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-out.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-out.txt new file mode 100644 index 0000000000..29338f3b5c --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-out.txt @@ -0,0 +1,17 @@ +maml.exe Train feat=Num lab=Lab tr=lr{t=- stat=+} loader=text{header+ col=Lab:0 col=Num:9-14} data=%Data% out=%Output% +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Beginning optimization +num vars: 7 +improvement criterion: Mean Improvement +Warning: Premature convergence occurred. The OptimizationTolerance may be set too small. ro equals zero. Is your function linear? +L1 regularization selected 7 of 7 weights. +Model trained with 500 training examples. +Residual Deviance: 458.96985 (on 493 degrees of freedom) +Null Deviance: 539.27637 (on 499 degrees of freedom) +AIC: 472.96985 +Warning: The number of parameters is too large. Cannot hold the variance-covariance matrix in memory. Skipping computation of standard errors and z-statistics of coefficients. Consider choosing a larger L1 regularizerto reduce the number of parameters. +Not training a calibrator because it is not needed. +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-summary.txt b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-summary.txt new file mode 100644 index 0000000000..e01bbe4f9c --- /dev/null +++ b/test/BaselineOutput/Common/Command/win-arm/CommandTrainingLrWithStats-summary.txt @@ -0,0 +1,15 @@ +Linear Binary Classification Predictor non-zero weights + +(Bias) -4.4267545 +education-num 2.102785 +age 1.9203715 +hours-per-week 1.8823524 +capital-gain 1.6711036 +capital-loss 0.9768483 +fnlwgt 0.3191273 + +*** MODEL STATISTICS SUMMARY *** +Count of training examples: 500 +Residual Deviance: 458.96985 +Null Deviance: 539.27637 +AIC: 472.96985 From 569f4c1b906de13c5601c5a368e4478c3d94fe06 Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Mon, 20 Nov 2023 12:45:08 -0700 Subject: [PATCH 16/17] fixed pack for mkl redist --- src/Microsoft.ML.Mkl.Redist/Microsoft.ML.Mkl.Redist.csproj | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/Microsoft.ML.Mkl.Redist/Microsoft.ML.Mkl.Redist.csproj b/src/Microsoft.ML.Mkl.Redist/Microsoft.ML.Mkl.Redist.csproj index 1aedb518d8..440caa98d1 100644 --- a/src/Microsoft.ML.Mkl.Redist/Microsoft.ML.Mkl.Redist.csproj +++ b/src/Microsoft.ML.Mkl.Redist/Microsoft.ML.Mkl.Redist.csproj @@ -7,6 +7,8 @@ $(MSBuildProjectName) contains the MKL library redistributed as a NuGet package. $(PackageTags) MLNET MKL + + false $(NoWarn);NU5127;NU5128 $(TargetsForTfmSpecificBuildOutput);CopyProjectReferencesToPackage From bf36f99d6aafe02e80cd686e78bcc8072a6902fc Mon Sep 17 00:00:00 2001 From: Michael Sharp Date: Mon, 20 Nov 2023 18:11:18 -0700 Subject: [PATCH 17/17] .NET update --- eng/Versions.props | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/eng/Versions.props b/eng/Versions.props index 9409892f5c..febd77a82e 100644 --- a/eng/Versions.props +++ b/eng/Versions.props @@ -78,7 +78,7 @@ 5.4.7 0.13.1 6.0.9 - 8.0.0-preview.3.23174.8 + 8.0.0 5.10.2 1.1.2-beta1.23431.1 8.0.0-beta.23265.1