785 строки
38 KiB
C#
785 строки
38 KiB
C#
// Licensed to the .NET Foundation under one or more agreements.
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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using System.Collections.Generic;
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using System.Globalization;
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using System.Threading;
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using Microsoft.ML;
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using Microsoft.ML.AutoML;
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using Microsoft.ML.CodeGenerator.CSharp;
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using Microsoft.ML.TestFramework;
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using Microsoft.ML.Trainers;
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using Xunit;
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using Xunit.Abstractions;
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namespace mlnet.Tests
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{
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public class TrainerGeneratorTests : BaseTestClass
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{
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public TrainerGeneratorTests(ITestOutputHelper output) : base(output)
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{
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}
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[Fact]
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public void CultureInvariantTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"LearningRate", 0.1f },
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{"NumberOfLeaves", 1 },
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};
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PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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//Set culture to deutsch.
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var currentCulture = Thread.CurrentThread.CurrentCulture;
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Thread.CurrentThread.CurrentCulture = new CultureInfo("de-DE");
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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Thread.CurrentThread.CurrentCulture = currentCulture;
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string expectedTrainerString = "LightGbm(learningRate:0.1f,numberOfLeaves:1,labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void LightGbmBinaryBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"LearningRate", 0.1f },
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{"NumberOfLeaves", 1 },
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};
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PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "LightGbm(learningRate:0.1f,numberOfLeaves:1,labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void LightGbmBinaryAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"LearningRate", 0.1f },
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{"NumLeaves", 1 },
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{"UseSoftmax", true }
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};
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PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "LightGbm(new LightGbmBinaryTrainer.Options(){LearningRate=0.1f,NumLeaves=1,UseSoftmax=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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string expectedUsings = "using Microsoft.ML.Trainers.LightGbm;\r\n";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void SymbolicSgdLogisticRegressionBinaryBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("SymbolicSgdLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "SymbolicSgdLogisticRegression(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void SymbolicSgdLogisticRegressionBinaryAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"LearningRate", 0.1f },
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};
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PipelineNode node = new PipelineNode("SymbolicSgdLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "SymbolicSgdLogisticRegression(new SymbolicSgdLogisticRegressionBinaryTrainer.Options(){LearningRate=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void SgdCalibratedBinaryBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("SgdCalibratedBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "SgdCalibrated(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void SgdCalibratedBinaryAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"Shuffle", true },
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};
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PipelineNode node = new PipelineNode("SgdCalibratedBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "SgdCalibrated(new SgdCalibratedTrainer.Options(){Shuffle=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void SdcaLogisticRegressionBinaryBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("SdcaLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "SdcaLogisticRegression(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void SdcaLogisticRegressionBinaryAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"BiasLearningRate", 0.1f },
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};
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PipelineNode node = new PipelineNode("SdcaLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "SdcaLogisticRegression(new SdcaLogisticRegressionBinaryTrainer.Options(){BiasLearningRate=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void SdcaMaximumEntropyMultiBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("SdcaMaximumEntropyMulti", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "SdcaMaximumEntropy(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void SdcaMaximumEntropyMultiAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"BiasLearningRate", 0.1f },
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};
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PipelineNode node = new PipelineNode("SdcaMaximumEntropyMulti", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "SdcaMaximumEntropy(new SdcaMaximumEntropyMulticlassTrainer.Options(){BiasLearningRate=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void SdcaRegressionBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("SdcaRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "Sdca(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void SdcaRegressionAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"BiasLearningRate", 0.1f },
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};
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PipelineNode node = new PipelineNode("SdcaRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "Sdca(new SdcaRegressionTrainer.Options(){BiasLearningRate=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void MatrixFactorizationBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("MatrixFactorization", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "MatrixFactorization(labelColumnName:\"Label\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void MatrixFactorizationAdvancedTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"MatrixColumnIndexColumnName","userId" },
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{"MatrixRowIndexColumnName","movieId" },
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{"LabelColumnName","rating" },
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{nameof(MatrixFactorizationTrainer.Options.NumberOfIterations), 10 },
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{nameof(MatrixFactorizationTrainer.Options.LearningRate), 0.01f },
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{nameof(MatrixFactorizationTrainer.Options.ApproximationRank), 8 },
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{nameof(MatrixFactorizationTrainer.Options.Lambda), 0.01f },
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{nameof(MatrixFactorizationTrainer.Options.LossFunction), MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression },
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{nameof(MatrixFactorizationTrainer.Options.Alpha), 1f },
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{nameof(MatrixFactorizationTrainer.Options.C), 0.00001f },
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};
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PipelineNode node = new PipelineNode("MatrixFactorization", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "MatrixFactorization(new MatrixFactorizationTrainer.Options(){MatrixColumnIndexColumnName=\"userId\",MatrixRowIndexColumnName=\"movieId\",LabelColumnName=\"rating\",NumberOfIterations=10,LearningRate=0.01f,ApproximationRank=8,Lambda=0.01f,LossFunction=MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression,Alpha=1f,C=1E-05f})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(new string[] { "using Microsoft.ML.Trainers;\r\n" }, actual.Item2);
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}
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[Fact]
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public void LbfgsPoissonRegressionBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("LbfgsPoissonRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "LbfgsPoissonRegression(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void LbfgsPoissonRegressionAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"MaximumNumberOfIterations", 1 },
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};
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PipelineNode node = new PipelineNode("LbfgsPoissonRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "LbfgsPoissonRegression(new LbfgsPoissonRegressionTrainer.Options(){MaximumNumberOfIterations=1,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void OlsRegressionBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("OlsRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "Ols(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void OlsRegressionAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"L2Regularization", 0.1f },
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};
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PipelineNode node = new PipelineNode("OlsRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "Ols(new OlsTrainer.Options(){L2Regularization=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void OnlineGradientDescentRegressionBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("OnlineGradientDescentRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "OnlineGradientDescent(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void OnlineGradientDescentRegressionAdvancedParameterTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>()
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{
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{"RecencyGainMulti", true },
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};
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PipelineNode node = new PipelineNode("OnlineGradientDescentRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
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string expectedTrainerString = "OnlineGradientDescent(new OnlineGradientDescentTrainer.Options(){RecencyGainMulti=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Equal(expectedUsings, actual.Item2[0]);
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}
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[Fact]
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public void LbfgsLogisticRegressionBinaryBasicTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();
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PipelineNode node = new PipelineNode("LbfgsLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
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Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
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CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
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var actual = codeGenerator.GenerateTrainerAndUsings();
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string expectedTrainerString = "LbfgsLogisticRegression(labelColumnName:\"Label\",featureColumnName:\"Features\")";
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Assert.Equal(expectedTrainerString, actual.Item1);
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Assert.Null(actual.Item2);
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}
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[Fact]
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public void LbfgsLogisticRegressionBinaryAdvancedParameterTest()
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{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"DenseOptimizer", true },
|
|
};
|
|
PipelineNode node = new PipelineNode("LbfgsLogisticRegressionBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
|
|
string expectedTrainerString = "LbfgsLogisticRegression(new LbfgsLogisticRegressionBinaryTrainer.Options(){DenseOptimizer=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void LbfgsMaximumEntropyMultiMultiBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("LbfgsMaximumEntropyMulti", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "LbfgsMaximumEntropy(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void LbfgsMaximumEntropyMultiAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"DenseOptimizer", true },
|
|
};
|
|
PipelineNode node = new PipelineNode("LbfgsMaximumEntropyMulti", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
|
|
string expectedTrainerString = "LbfgsMaximumEntropy(new LbfgsMaximumEntropyMulticlassTrainer.Options(){DenseOptimizer=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void LinearSvmBinaryBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("LinearSvmBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "LinearSvm(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void LinearSvmBinaryParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"NoBias", true },
|
|
};
|
|
PipelineNode node = new PipelineNode("LinearSvmBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers;\r\n ";
|
|
string expectedTrainerString = "LinearSvm(new LinearSvmTrainer.Options(){NoBias=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void FastTreeTweedieRegressionBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("FastTreeTweedieRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "FastTreeTweedie(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void FastTreeTweedieRegressionAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shrinkage", 0.1f },
|
|
};
|
|
PipelineNode node = new PipelineNode("OnlineGradientDescentRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers;\r\n";
|
|
string expectedTrainerString = "OnlineGradientDescent(new OnlineGradientDescentTrainer.Options(){Shrinkage=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void FastTreeRegressionBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("FastTreeRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "FastTree(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void FastTreeRegressionAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shrinkage", 0.1f },
|
|
};
|
|
PipelineNode node = new PipelineNode("FastTreeRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers.FastTree;\r\n";
|
|
string expectedTrainerString = "FastTree(new FastTreeRegressionTrainer.Options(){Shrinkage=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void FastTreeBinaryBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("FastTreeBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "FastTree(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void FastTreeBinaryAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shrinkage", 0.1f },
|
|
};
|
|
PipelineNode node = new PipelineNode("FastTreeBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers.FastTree;\r\n";
|
|
string expectedTrainerString = "FastTree(new FastTreeBinaryTrainer.Options(){Shrinkage=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void FastForestRegressionBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("FastForestRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "FastForest(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void FastForestRegressionAdvancedParameterTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shrinkage", 0.1f },
|
|
};
|
|
PipelineNode node = new PipelineNode("FastForestRegression", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers.FastTree;\r\n";
|
|
string expectedTrainerString = "FastForest(new FastForestRegression.Options(){Shrinkage=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void FastForestBinaryBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("FastForestBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "FastForest(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void FastForestBinaryAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shrinkage", 0.1f },
|
|
};
|
|
PipelineNode node = new PipelineNode("FastForestBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers.FastTree;\r\n";
|
|
string expectedTrainerString = "FastForest(new FastForestClassification.Options(){Shrinkage=0.1f,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
|
|
[Fact]
|
|
public void AveragedPerceptronBinaryBasicTest()
|
|
{
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("AveragedPerceptronBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "AveragedPerceptron(labelColumnName:\"Label\",featureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void AveragedPerceptronBinaryAdvancedParameterTest()
|
|
{
|
|
|
|
var context = new MLContext();
|
|
|
|
var elementProperties = new Dictionary<string, object>()
|
|
{
|
|
{"Shuffle", true },
|
|
};
|
|
PipelineNode node = new PipelineNode("AveragedPerceptronBinary", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
var expectedUsings = "using Microsoft.ML.Trainers;\r\n ";
|
|
string expectedTrainerString = "AveragedPerceptron(new AveragedPerceptronTrainer.Options(){Shuffle=true,LabelColumnName=\"Label\",FeatureColumnName=\"Features\"})";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Equal(expectedUsings, actual.Item2[0]);
|
|
|
|
}
|
|
|
|
[Fact]
|
|
public void ImageClassificationTrainerBasicTest()
|
|
{
|
|
var elementProperties = new Dictionary<string, object>();
|
|
PipelineNode node = new PipelineNode("ImageClassification", PipelineNodeType.Trainer, default(string[]), default(string), elementProperties);
|
|
Pipeline pipeline = new Pipeline(new PipelineNode[] { node });
|
|
CodeGenerator codeGenerator = new CodeGenerator(pipeline, null, null);
|
|
var actual = codeGenerator.GenerateTrainerAndUsings();
|
|
string expectedTrainerString = "ImageClassification(LabelColumnName:\"Label\",FeatureColumnName:\"Features\")";
|
|
Assert.Equal(expectedTrainerString, actual.Item1);
|
|
Assert.Null(actual.Item2);
|
|
}
|
|
}
|
|
}
|