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srsaggamsrsaggam
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added tests (dotnet#187)
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‎src/mlnet.Test/CodeGenTests.cs

+2-156
Original file line numberDiff line numberDiff line change
@@ -24,7 +24,7 @@ public void TrainerGeneratorBasicNamedParameterTest()
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{"LearningRate", 0.1f },
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{"NumLeaves", 1 },
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};
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PipelineNode node = new PipelineNode("LightGbmBinary", PipelineNodeType.Trainer, new string[] { "Label" }, default(string), elementProperties);
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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();
@@ -44,7 +44,7 @@ public void TrainerGeneratorBasicAdvancedParameterTest()
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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, new string[] { "Label" }, default(string), elementProperties);
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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();
@@ -169,159 +169,5 @@ public void TrainerComplexParameterTest()
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Assert.AreEqual(expectedUsings, actual.Item2);
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}
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#region Transform Tests
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[TestMethod]
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public void MissingValueReplacingTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();//categorical
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PipelineNode node = new PipelineNode("MissingValueReplacing", PipelineNodeType.Transform, new string[] { "categorical_column_1" }, new string[] { "categorical_column_1" }, 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.GenerateTransformsAndUsings();
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var expectedTransform = "ReplaceMissingValues(new []{new MissingValueReplacingTransformer.ColumnInfo(\"categorical_column_1\",\"categorical_column_1\")})";
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string expectedUsings = "using Microsoft.ML.Transforms;\r\n";
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void OneHotEncodingTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();//categorical
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PipelineNode node = new PipelineNode("OneHotEncoding", PipelineNodeType.Transform, new string[] { "categorical_column_1" }, new string[] { "categorical_column_1" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Categorical.OneHotEncoding(new []{new OneHotEncodingEstimator.ColumnInfo(\"categorical_column_1\",\"categorical_column_1\")})";
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var expectedUsings = "using Microsoft.ML.Transforms.Categorical;\r\n";
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void NormalizingTest()
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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("Normalizing", PipelineNodeType.Transform, new string[] { "numeric_column_1" }, new string[] { "numeric_column_1_copy" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Normalize(\"numeric_column_1_copy\",\"numeric_column_1\")";
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string expectedUsings = null;
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void ColumnConcatenatingTest()
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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("ColumnConcatenating", PipelineNodeType.Transform, new string[] { "numeric_column_1", "numeric_column_2" }, new string[] { "Features" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Concatenate(\"Features\",new []{\"numeric_column_1\",\"numeric_column_2\"})";
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string expectedUsings = null;
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void ColumnCopyingTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();//nume to num feature 2
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PipelineNode node = new PipelineNode("ColumnCopying", PipelineNodeType.Transform, new string[] { "numeric_column_1" }, new string[] { "numeric_column_2" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "CopyColumns(\"numeric_column_2\",\"numeric_column_1\")";
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string expectedUsings = null;
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void MissingValueIndicatingTest()
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{
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var context = new MLContext();
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var elementProperties = new Dictionary<string, object>();//numeric feature
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PipelineNode node = new PipelineNode("MissingValueIndicating", PipelineNodeType.Transform, new string[] { "numeric_column_1" }, new string[] { "numeric_column_1" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "IndicateMissingValues(new []{(\"numeric_column_1\",\"numeric_column_1\")})";
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string expectedUsings = null;
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void OneHotHashEncodingTest()
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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("OneHotHashEncoding", PipelineNodeType.Transform, new string[] { "Categorical_column_1" }, new string[] { "Categorical_column_1" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Categorical.OneHotHashEncoding(new []{new OneHotHashEncodingEstimator.ColumnInfo(\"Categorical_column_1\",\"Categorical_column_1\")})";
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var expectedUsings = "using Microsoft.ML.Transforms.Categorical;\r\n";
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void TextFeaturizingTest()
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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("TextFeaturizing", PipelineNodeType.Transform, new string[] { "Text_column_1" }, new string[] { "Text_column_1" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Text.FeaturizeText(\"Text_column_1\",\"Text_column_1\")";
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string expectedUsings = null;
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void TypeConvertingTest()
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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("TypeConverting", PipelineNodeType.Transform, new string[] { "I4_column_1" }, new string[] { "R4_column_1" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Conversion.ConvertType(new []{new TypeConvertingTransformer.ColumnInfo(\"R4_column_1\",DataKind.R4,\"I4_column_1\")})";
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string expectedUsings = "using Microsoft.ML.Transforms.Conversions;\r\n";
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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[TestMethod]
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public void ValueToKeyMappingTest()
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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("ValueToKeyMapping", PipelineNodeType.Transform, new string[] { "Label" }, new string[] { "Label" }, 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.GenerateTransformsAndUsings();
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string expectedTransform = "Conversion.MapValueToKey(\"Label\",\"Label\")";
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var expectedUsings = "using Microsoft.ML.Transforms.Conversions;\r\n";
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Assert.AreEqual(expectedTransform, actual[0].Item1);
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Assert.AreEqual(expectedUsings, actual[0].Item2);
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}
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#endregion
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}
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}

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