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Generated Project new structure. (dotnet#305)
* added new templates * writing files to disck * change path * added new templates * misisng braces * fix bugs * format code * added util methods for solution file creation and addition of projects to it * added extra packages to project files * new tests * added correct path for sln * build fix * fix build
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‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.ConsoleHelperFileContentTest.approved.txt

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//*****************************************************************************************
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//* *
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//* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. *
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//* *
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//*****************************************************************************************
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using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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namespace TestNamespace.Train
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{
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public static class ConsoleHelper
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{
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public static void PrintRegressionMetrics(RegressionMetrics metrics)
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{
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Console.WriteLine($"*************************************************");
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Console.WriteLine($"* Metrics for regression model ");
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Console.WriteLine($"*------------------------------------------------");
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Console.WriteLine($"* LossFn: {metrics.LossFn:0.##}");
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Console.WriteLine($"* R2 Score: {metrics.RSquared:0.##}");
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Console.WriteLine($"* Absolute loss: {metrics.L1:#.##}");
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Console.WriteLine($"* Squared loss: {metrics.L2:#.##}");
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Console.WriteLine($"* RMS loss: {metrics.Rms:#.##}");
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Console.WriteLine($"*************************************************");
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}
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public static void PrintRegressionFoldsAverageMetrics(TrainCatalogBase.CrossValidationResult<RegressionMetrics>[] crossValidationResults)
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{
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var L1 = crossValidationResults.Select(r => r.Metrics.L1);
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var L2 = crossValidationResults.Select(r => r.Metrics.L2);
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var RMS = crossValidationResults.Select(r => r.Metrics.L1);
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var lossFunction = crossValidationResults.Select(r => r.Metrics.LossFn);
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var R2 = crossValidationResults.Select(r => r.Metrics.RSquared);
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Console.WriteLine($"*************************************************************************************************************");
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Console.WriteLine($"* Metrics for Regression model ");
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Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
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Console.WriteLine($"* Average L1 Loss: {L1.Average():0.###} ");
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Console.WriteLine($"* Average L2 Loss: {L2.Average():0.###} ");
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Console.WriteLine($"* Average RMS: {RMS.Average():0.###} ");
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Console.WriteLine($"* Average Loss Function: {lossFunction.Average():0.###} ");
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Console.WriteLine($"* Average R-squared: {R2.Average():0.###} ");
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Console.WriteLine($"*************************************************************************************************************");
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}
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public static void PrintBinaryClassificationMetrics(BinaryClassificationMetrics metrics)
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{
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Console.WriteLine($"************************************************************");
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Console.WriteLine($"* Metrics for binary classification model ");
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Console.WriteLine($"*-----------------------------------------------------------");
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Console.WriteLine($"* Accuracy: {metrics.Accuracy:P2}");
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Console.WriteLine($"* Auc: {metrics.Auc:P2}");
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Console.WriteLine($"************************************************************");
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}
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public static void PrintBinaryClassificationFoldsAverageMetrics(
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TrainCatalogBase.CrossValidationResult<BinaryClassificationMetrics>[] crossValResults)
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{
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var metricsInMultipleFolds = crossValResults.Select(r => r.Metrics);
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var AccuracyValues = metricsInMultipleFolds.Select(m => m.Accuracy);
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var AccuracyAverage = AccuracyValues.Average();
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var AccuraciesStdDeviation = CalculateStandardDeviation(AccuracyValues);
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var AccuraciesConfidenceInterval95 = CalculateConfidenceInterval95(AccuracyValues);
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Console.WriteLine($"*************************************************************************************************************");
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Console.WriteLine($"* Metrics for Binary Classification model ");
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Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
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Console.WriteLine($"* Average Accuracy: {AccuracyAverage:0.###} - Standard deviation: ({AccuraciesStdDeviation:#.###}) - Confidence Interval 95%: ({AccuraciesConfidenceInterval95:#.###})");
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Console.WriteLine($"*************************************************************************************************************");
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}
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public static void PrintMulticlassClassificationFoldsAverageMetrics(
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TrainCatalogBase.CrossValidationResult<MultiClassClassifierMetrics>[] crossValResults)
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{
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var metricsInMultipleFolds = crossValResults.Select(r => r.Metrics);
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var microAccuracyValues = metricsInMultipleFolds.Select(m => m.AccuracyMicro);
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var microAccuracyAverage = microAccuracyValues.Average();
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var microAccuraciesStdDeviation = CalculateStandardDeviation(microAccuracyValues);
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var microAccuraciesConfidenceInterval95 = CalculateConfidenceInterval95(microAccuracyValues);
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var macroAccuracyValues = metricsInMultipleFolds.Select(m => m.AccuracyMacro);
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var macroAccuracyAverage = macroAccuracyValues.Average();
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var macroAccuraciesStdDeviation = CalculateStandardDeviation(macroAccuracyValues);
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var macroAccuraciesConfidenceInterval95 = CalculateConfidenceInterval95(macroAccuracyValues);
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var logLossValues = metricsInMultipleFolds.Select(m => m.LogLoss);
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var logLossAverage = logLossValues.Average();
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var logLossStdDeviation = CalculateStandardDeviation(logLossValues);
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var logLossConfidenceInterval95 = CalculateConfidenceInterval95(logLossValues);
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var logLossReductionValues = metricsInMultipleFolds.Select(m => m.LogLossReduction);
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var logLossReductionAverage = logLossReductionValues.Average();
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var logLossReductionStdDeviation = CalculateStandardDeviation(logLossReductionValues);
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var logLossReductionConfidenceInterval95 = CalculateConfidenceInterval95(logLossReductionValues);
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Console.WriteLine($"*************************************************************************************************************");
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Console.WriteLine($"* Metrics for Multi-class Classification model ");
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Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
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Console.WriteLine($"* Average MicroAccuracy: {microAccuracyAverage:0.###} - Standard deviation: ({microAccuraciesStdDeviation:#.###}) - Confidence Interval 95%: ({microAccuraciesConfidenceInterval95:#.###})");
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Console.WriteLine($"* Average MacroAccuracy: {macroAccuracyAverage:0.###} - Standard deviation: ({macroAccuraciesStdDeviation:#.###}) - Confidence Interval 95%: ({macroAccuraciesConfidenceInterval95:#.###})");
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Console.WriteLine($"* Average LogLoss: {logLossAverage:#.###} - Standard deviation: ({logLossStdDeviation:#.###}) - Confidence Interval 95%: ({logLossConfidenceInterval95:#.###})");
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Console.WriteLine($"* Average LogLossReduction: {logLossReductionAverage:#.###} - Standard deviation: ({logLossReductionStdDeviation:#.###}) - Confidence Interval 95%: ({logLossReductionConfidenceInterval95:#.###})");
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Console.WriteLine($"*************************************************************************************************************");
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}
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public static double CalculateStandardDeviation(IEnumerable<double> values)
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{
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double average = values.Average();
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double sumOfSquaresOfDifferences = values.Select(val => (val - average) * (val - average)).Sum();
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double standardDeviation = Math.Sqrt(sumOfSquaresOfDifferences / (values.Count() - 1));
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return standardDeviation;
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}
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public static double CalculateConfidenceInterval95(IEnumerable<double> values)
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{
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double confidenceInterval95 = 1.96 * CalculateStandardDeviation(values) / Math.Sqrt((values.Count() - 1));
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return confidenceInterval95;
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}
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}
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}

‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.ModelProjectFileContentTest.approved.txt

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netcoreapp2.1</TargetFramework>
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</PropertyGroup>
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<PropertyGroup>
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<RestoreSources>
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https://api.nuget.org/v3/index.json;
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</RestoreSources>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML" Version="0.11.0" />
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</ItemGroup>
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<ItemGroup>
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<None Update="MLModel.zip">
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<CopyToOutputDirectory>PreserveNewest</CopyToOutputDirectory>
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</None>
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</ItemGroup>
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</Project>

‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.ObservationCSFileContentTest.approved.txt

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//*****************************************************************************************
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//* *
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//* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. *
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//* *
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//*****************************************************************************************
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using Microsoft.ML.Data;
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namespace TestNamespace.Model.DataModels
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{
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public class SampleObservation
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{
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[ColumnName("Label"), LoadColumn(0)]
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public bool Label { get; set; }
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[ColumnName("col1"), LoadColumn(1)]
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public float Col1 { get; set; }
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[ColumnName("col2"), LoadColumn(0)]
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public float Col2 { get; set; }
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[ColumnName("col3"), LoadColumn(0)]
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public string Col3 { get; set; }
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[ColumnName("col4"), LoadColumn(0)]
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public int Col4 { get; set; }
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[ColumnName("col5"), LoadColumn(0)]
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public uint Col5 { get; set; }
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}
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}

‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.PredictProgramCSFileContentTest.approved.txt

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//*****************************************************************************************
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//* *
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//* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. *
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//* *
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//*****************************************************************************************
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using System;
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using System.IO;
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using System.Linq;
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using System.Collections.Generic;
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using Microsoft.ML;
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using Microsoft.ML.Data;
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using Microsoft.Data.DataView;
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using TestNamespace.Model.DataModels;
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namespace TestNamespace.Predict
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{
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class Program
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{
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//Machine Learning model to load and use for predictions
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private const string MODEL_FILEPATH = @"MLModel.zip";
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//Dataset to use for predictions
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private const string DATA_FILEPATH = @"x:\dummypath\dummy_test.csv";
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static void Main(string[] args)
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{
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MLContext mlContext = new MLContext();
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//Load ML Model from .zip file
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ITransformer mlModel = LoadModelFromFile(mlContext, MODEL_FILEPATH);
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// Create sample data to do a single prediction with it
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SampleObservation sampleData = CreateSingleDataSample(mlContext, DATA_FILEPATH);
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// Test a single prediction
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Predict(mlContext, mlModel, sampleData);
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Console.WriteLine("=============== End of process, hit any key to finish ===============");
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Console.ReadKey();
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}
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private static void Predict(MLContext mlContext, ITransformer mlModel, SampleObservation sampleData)
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{
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// Create prediction engine related to the loaded ML model
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var predEngine = mlModel.CreatePredictionEngine<SampleObservation, SamplePrediction>(mlContext);
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// Try a single prediction
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var predictionResult = predEngine.Predict(sampleData);
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Console.WriteLine($"Single Prediction --> Actual value: {sampleData.Label} | Predicted value: {predictionResult.Prediction}");
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}
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private static ITransformer LoadModelFromFile(MLContext mlContext, string modelFilePath)
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{
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ITransformer mlModel;
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using (var stream = new FileStream(modelFilePath, FileMode.Open, FileAccess.Read, FileShare.Read))
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{
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mlModel = mlContext.Model.Load(stream);
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}
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return mlModel;
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}
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// Method to load single row of data to try a single prediction
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// You can change this code and create your own sample data here (Hardcoded or from any source)
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private static SampleObservation CreateSingleDataSample(MLContext mlContext, string dataFilePath)
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{
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// Read dataset to get a single row for trying a prediction
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IDataView dataView = mlContext.Data.LoadFromTextFile<SampleObservation>(
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path: dataFilePath,
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hasHeader: true,
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separatorChar: ',');
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// Here (SampleObservation object) you could provide new test data, hardcoded or from the end-user application, instead of the row from the file.
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SampleObservation sampleForPrediction = mlContext.Data.CreateEnumerable<SampleObservation>(dataView, false)
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.First();
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return sampleForPrediction;
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}
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}
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}

‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.PredictProjectFileContentTest.approved.txt

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<OutputType>Exe</OutputType>
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<TargetFramework>netcoreapp2.1</TargetFramework>
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.ML" Version="0.11.0" />
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<PackageReference Include="Microsoft.ML.LightGBM" Version="0.11.0" />
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<PackageReference Include="Microsoft.ML.HalLearners" Version="0.11.0" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\TestNamespace.Model\TestNamespace.Model.csproj" />
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</ItemGroup>
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</Project>

‎src/mlnet.Test/ApprovalTests/ConsoleCodeGeneratorTests.PredictionCSFileContentTest.approved.txt

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//*****************************************************************************************
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//* *
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//* This is an auto-generated file by Microsoft ML.NET CLI (Command-Line Interface) tool. *
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//* *
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//*****************************************************************************************
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using Microsoft.ML.Data;
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namespace TestNamespace.Model.DataModels
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{
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public class SamplePrediction
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{
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// ColumnName attribute is used to change the column name from
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// its default value, which is the name of the field.
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[ColumnName("PredictedLabel")]
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public bool Prediction { get; set; }
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public float Score { get; set; }
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}
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}

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