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| 1 | +using System.Collections.Generic; |
| 2 | +using Microsoft.ML; |
| 3 | +using Microsoft.ML.Auto; |
| 4 | +using Microsoft.ML.Data; |
| 5 | +using Microsoft.VisualStudio.TestTools.UnitTesting; |
| 6 | +using Microsoft.ML.CLI; |
| 7 | +using System; |
| 8 | + |
| 9 | +namespace mlnet.Test |
| 10 | +{ |
| 11 | + [TestClass] |
| 12 | + public class CodeGeneratorTests |
| 13 | + { |
| 14 | + [TestMethod] |
| 15 | + public void TrainerGeneratorBasicNamedParameterTest() |
| 16 | + { |
| 17 | + var context = new MLContext(); |
| 18 | + |
| 19 | + var elementProperties = new Dictionary<string, object>() |
| 20 | + { |
| 21 | + {"LearningRate", 0.1f }, |
| 22 | + {"NumLeaves", 1 }, |
| 23 | + }; |
| 24 | + PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, new string[] { "Label" }, default(string), elementProperties); |
| 25 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 26 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, null); |
| 27 | + var actual = codeGenerator.GenerateTrainer(); |
| 28 | + string expected = "LightGbm(learningRate:0.1f,numLeaves:1,labelColumn:\"Label\",featureColumn:\"Features\");"; |
| 29 | + Assert.AreEqual(expected, actual); |
| 30 | + } |
| 31 | + |
| 32 | + [TestMethod] |
| 33 | + public void TrainerGeneratorBasicAdvancedParameterTest() |
| 34 | + { |
| 35 | + var context = new MLContext(); |
| 36 | + |
| 37 | + var elementProperties = new Dictionary<string, object>() |
| 38 | + { |
| 39 | + {"LearningRate", 0.1f }, |
| 40 | + {"NumLeaves", 1 }, |
| 41 | + {"UseSoftmax", true } |
| 42 | + }; |
| 43 | + PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, new string[] { "Label" }, default(string), elementProperties); |
| 44 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 45 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, null); |
| 46 | + var actual = codeGenerator.GenerateTrainer(); |
| 47 | + string expected = "LightGbm(new LightGbm.Options(){LearningRate=0.1f,NumLeaves=1,UseSoftmax=true,LabelColumn=\"Label\",FeatureColumn=\"Features\"});"; |
| 48 | + Assert.AreEqual(expected, actual); |
| 49 | + } |
| 50 | + |
| 51 | + [TestMethod] |
| 52 | + public void TransformGeneratorBasicTest() |
| 53 | + { |
| 54 | + var context = new MLContext(); |
| 55 | + var elementProperties = new Dictionary<string, object>(); |
| 56 | + PipelineNode node = new PipelineNode("Normalizing", PipelineNodeType.Transform, new string[] { "Label" }, new string[] { "Label" }, elementProperties); |
| 57 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 58 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, null); |
| 59 | + var actual = codeGenerator.GenerateTransforms(); |
| 60 | + string expected = "Normalize(\"Label\",\"Label\")"; |
| 61 | + Assert.AreEqual(expected, actual[0]); |
| 62 | + } |
| 63 | + |
| 64 | + [TestMethod] |
| 65 | + public void ClassLabelGenerationBasicTest() |
| 66 | + { |
| 67 | + List<(TextLoader.Column, ColumnPurpose)> list = new List<(TextLoader.Column, ColumnPurpose)>() |
| 68 | + { |
| 69 | + (new TextLoader.Column(){ Name = "Label", Source = new TextLoader.Range[]{new TextLoader.Range(0) }, Type = DataKind.Bool }, ColumnPurpose.Label), |
| 70 | + }; |
| 71 | + ColumnInferenceResult result = new ColumnInferenceResult(list, false, false, ",", true, true); |
| 72 | + |
| 73 | + CodeGenerator codeGenerator = new CodeGenerator(null, result); |
| 74 | + var actual = codeGenerator.GenerateClassLabels(); |
| 75 | + var expected1 = "[ColumnName(\"Label\")]"; |
| 76 | + var expected2 = "public bool Label{get; set;}"; |
| 77 | + |
| 78 | + Assert.AreEqual(expected1, actual[0]); |
| 79 | + Assert.AreEqual(expected2, actual[1]); |
| 80 | + } |
| 81 | + |
| 82 | + [TestMethod] |
| 83 | + public void GenerateUsingsBasicTest() |
| 84 | + { |
| 85 | + var context = new MLContext(); |
| 86 | + var elementProperties = new Dictionary<string, object>(); |
| 87 | + PipelineNode node = new PipelineNode("TypeConverting", PipelineNodeType.Transform, new string[] { "Label" }, new string[] { "Label" }, elementProperties); |
| 88 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 89 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, null); |
| 90 | + var actual = codeGenerator.GenerateUsings(); |
| 91 | + string expected = "using Microsoft.ML.Transforms.Conversions;\r\n"; |
| 92 | + Assert.AreEqual(expected, actual); |
| 93 | + } |
| 94 | + |
| 95 | + [TestMethod] |
| 96 | + public void ColumnGenerationTest() |
| 97 | + { |
| 98 | + List<(TextLoader.Column, ColumnPurpose)> list = new List<(TextLoader.Column, ColumnPurpose)>() |
| 99 | + { |
| 100 | + (new TextLoader.Column(){ Name = "Label", Source = new TextLoader.Range[]{new TextLoader.Range(0) }, Type = DataKind.Bool }, ColumnPurpose.Label), |
| 101 | + (new TextLoader.Column(){ Name = "Features", Source = new TextLoader.Range[]{new TextLoader.Range(1) }, Type = DataKind.R4 }, ColumnPurpose.NumericFeature), |
| 102 | + }; |
| 103 | + ColumnInferenceResult result = new ColumnInferenceResult(list, false, false, ",", true, true); |
| 104 | + |
| 105 | + var context = new MLContext(); |
| 106 | + var elementProperties = new Dictionary<string, object>(); |
| 107 | + PipelineNode node = new PipelineNode("Normalizing", PipelineNodeType.Transform, new string[] { "Label" }, new string[] { "Label" }, elementProperties); |
| 108 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 109 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, result); |
| 110 | + var actual = codeGenerator.GenerateColumns(); |
| 111 | + Assert.AreEqual(actual.Count, 2); |
| 112 | + string expectedColumn1 = "new Column(\"Label\",DataKind.BL,0),"; |
| 113 | + string expectedColumn2 = "new Column(\"Features\",DataKind.R4,1),"; |
| 114 | + Assert.AreEqual(expectedColumn1, actual[0]); |
| 115 | + Assert.AreEqual(expectedColumn2, actual[1]); |
| 116 | + } |
| 117 | + |
| 118 | + [TestMethod] |
| 119 | + public void TrainerComplexParameterTest() |
| 120 | + { |
| 121 | + var context = new MLContext(); |
| 122 | + |
| 123 | + var elementProperties = new Dictionary<string, object>() |
| 124 | + { |
| 125 | + {"TreeBooster", new CustomProperty(){Properties= new Dictionary<string, object>(), Name = "TreeBooster"} }, |
| 126 | + }; |
| 127 | + PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, new string[] { "Label" }, default(string), elementProperties); |
| 128 | + Pipeline pipeline = new Pipeline(new PipelineNode[] { node }); |
| 129 | + CodeGenerator codeGenerator = new CodeGenerator(pipeline, null); |
| 130 | + var actual = codeGenerator.GenerateTrainer(); |
| 131 | + string expected = "LightGbm(new LightGbm.Options(){TreeBooster=new TreeBooster(){},LabelColumn=\"Label\",FeatureColumn=\"Features\"});"; |
| 132 | + Assert.AreEqual(expected, actual); |
| 133 | + |
| 134 | + } |
| 135 | + |
| 136 | + } |
| 137 | +} |
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