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Refactor the orchestration of AutoML calls (dotnet#272)
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5 files changed

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-178
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5 files changed

+250
-178
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‎src/mlnet/AutoML/AutoMLEngine.cs

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// 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 Microsoft.Data.DataView;
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using Microsoft.ML.Auto;
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using Microsoft.ML.CLI.Data;
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using Microsoft.ML.CLI.Utilities;
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using NLog;
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namespace Microsoft.ML.CLI.CodeGenerator
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{
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internal class AutoMLEngine : IAutoMLEngine
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{
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private NewCommandSettings settings;
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private TaskKind taskKind;
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private static Logger logger = LogManager.GetCurrentClassLogger();
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public AutoMLEngine(NewCommandSettings settings)
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{
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this.settings = settings;
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this.taskKind = Utils.GetTaskKind(settings.MlTask);
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}
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public ColumnInferenceResults InferColumns(MLContext context)
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{
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//Check what overload method of InferColumns needs to be called.
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logger.Log(LogLevel.Info, Strings.InferColumns);
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ColumnInferenceResults columnInference = null;
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var dataset = settings.Dataset.FullName;
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if (settings.LabelColumnName != null)
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{
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columnInference = context.Auto().InferColumns(dataset, settings.LabelColumnName, groupColumns: false);
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}
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else
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{
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columnInference = context.Auto().InferColumns(dataset, settings.LabelColumnIndex, hasHeader: settings.HasHeader, groupColumns: false);
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}
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return columnInference;
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}
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(Pipeline, ITransformer) IAutoMLEngine.ExploreModels(MLContext context, IDataView trainData, IDataView validationData, string labelName)
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{
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ITransformer model = null;
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Pipeline pipeline = null;
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if (taskKind == TaskKind.BinaryClassification)
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{
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var progressReporter = new ProgressHandlers.BinaryClassificationHandler();
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var result = context.Auto()
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.CreateBinaryClassificationExperiment(new BinaryExperimentSettings()
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{
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MaxExperimentTimeInSeconds = settings.MaxExplorationTime,
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ProgressHandler = progressReporter
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})
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.Execute(trainData, validationData, new ColumnInformation() { LabelColumn = labelName });
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logger.Log(LogLevel.Info, Strings.RetrieveBestPipeline);
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var bestIteration = result.Best();
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pipeline = bestIteration.Pipeline;
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model = bestIteration.Model;
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}
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if (taskKind == TaskKind.Regression)
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{
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var progressReporter = new ProgressHandlers.RegressionHandler();
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var result = context.Auto()
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.CreateRegressionExperiment(new RegressionExperimentSettings()
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{
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MaxExperimentTimeInSeconds = settings.MaxExplorationTime,
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ProgressHandler = progressReporter
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}).Execute(trainData, validationData, new ColumnInformation() { LabelColumn = labelName });
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logger.Log(LogLevel.Info, Strings.RetrieveBestPipeline);
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var bestIteration = result.Best();
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pipeline = bestIteration.Pipeline;
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model = bestIteration.Model;
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}
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if (taskKind == TaskKind.MulticlassClassification)
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{
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var progressReporter = new ProgressHandlers.MulticlassClassificationHandler();
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var experimentSettings = new MulticlassExperimentSettings()
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{
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MaxExperimentTimeInSeconds = settings.MaxExplorationTime,
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ProgressHandler = progressReporter
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};
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// Inclusion list for currently supported learners. Need to remove once we have codegen support for all other learners.
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experimentSettings.Trainers.Clear();
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experimentSettings.Trainers.Add(MulticlassClassificationTrainer.LightGbm);
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experimentSettings.Trainers.Add(MulticlassClassificationTrainer.LogisticRegression);
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experimentSettings.Trainers.Add(MulticlassClassificationTrainer.StochasticDualCoordinateAscent);
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var result = context.Auto()
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.CreateMulticlassClassificationExperiment(experimentSettings)
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.Execute(trainData, validationData, new ColumnInformation() { LabelColumn = labelName });
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logger.Log(LogLevel.Info, Strings.RetrieveBestPipeline);
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var bestIteration = result.Best();
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pipeline = bestIteration.Pipeline;
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model = bestIteration.Model;
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}
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return (pipeline, model);
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}
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}
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}

‎src/mlnet/AutoML/IAutoMLEngine.cs

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// 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 Microsoft.Data.DataView;
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using Microsoft.ML.Auto;
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namespace Microsoft.ML.CLI.CodeGenerator
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{
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internal interface IAutoMLEngine
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{
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ColumnInferenceResults InferColumns(MLContext context);
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(Pipeline, ITransformer) ExploreModels(MLContext context, IDataView trainData, IDataView validationData, string labelName);
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}
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}

‎src/mlnet/CodeGenerator/CodeGenerationHelper.cs

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// 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;
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using System.IO;
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using Microsoft.Data.DataView;
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using Microsoft.ML.Auto;
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using Microsoft.ML.CLI.CodeGenerator.CSharp;
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using Microsoft.ML.CLI.Data;
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using Microsoft.ML.CLI.Utilities;
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using Microsoft.ML.Data;
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using NLog;
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namespace Microsoft.ML.CLI.CodeGenerator
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{
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internal class CodeGenerationHelper
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{
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private IAutoMLEngine automlEngine;
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private NewCommandSettings settings;
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private static Logger logger = LogManager.GetCurrentClassLogger();
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private TaskKind taskKind;
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public CodeGenerationHelper(IAutoMLEngine automlEngine, NewCommandSettings settings)
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{
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this.automlEngine = automlEngine;
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this.settings = settings;
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this.taskKind = Utils.GetTaskKind(settings.MlTask);
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}
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public void GenerateCode()
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{
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var context = new MLContext();
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// Infer columns
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ColumnInferenceResults columnInference = null;
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try
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{
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columnInference = automlEngine.InferColumns(context);
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}
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catch (Exception e)
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{
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logger.Log(LogLevel.Error, $"{Strings.InferColumnError}");
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logger.Log(LogLevel.Error, e.Message);
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logger.Log(LogLevel.Debug, e.ToString());
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logger.Log(LogLevel.Error, Strings.Exiting);
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return;
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}
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// Sanitize columns
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Array.ForEach(columnInference.TextLoaderOptions.Columns, t => t.Name = Utils.Sanitize(t.Name));
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var sanitizedLabelName = Utils.Sanitize(columnInference.ColumnInformation.LabelColumn);
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// Load data
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(IDataView trainData, IDataView validationData) = LoadData(context, columnInference.TextLoaderOptions);
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// Explore the models
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(Pipeline, ITransformer) result = default;
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Console.WriteLine($"{Strings.ExplorePipeline}: {settings.MlTask}");
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try
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{
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result = automlEngine.ExploreModels(context, trainData, validationData, sanitizedLabelName);
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}
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catch (Exception e)
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{
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logger.Log(LogLevel.Error, $"{Strings.ExplorePipelineException}:");
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logger.Log(LogLevel.Error, e.Message);
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logger.Log(LogLevel.Debug, e.ToString());
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logger.Log(LogLevel.Error, Strings.Exiting);
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return;
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}
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//Get the best pipeline
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Pipeline pipeline = null;
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pipeline = result.Item1;
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var model = result.Item2;
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// Save the model
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logger.Log(LogLevel.Info, Strings.SavingBestModel);
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var modelPath = new FileInfo(Path.Combine(settings.OutputPath.FullName, "model.zip"));
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Utils.SaveModel(model, modelPath, context);
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// Generate the Project
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GenerateProject(columnInference, pipeline, sanitizedLabelName, modelPath);
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}
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internal void GenerateProject(ColumnInferenceResults columnInference, Pipeline pipeline, string labelName, FileInfo modelPath)
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{
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//Generate code
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logger.Log(LogLevel.Info, $"{Strings.GenerateProject} : {settings.OutputPath.FullName}");
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var codeGenerator = new CodeGenerator.CSharp.CodeGenerator(
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pipeline,
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columnInference,
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new CodeGeneratorSettings()
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{
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TrainDataset = settings.Dataset.FullName,
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MlTask = taskKind,
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TestDataset = settings.TestDataset?.FullName,
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OutputName = settings.Name,
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OutputBaseDir = settings.OutputPath.FullName,
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LabelName = labelName,
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ModelPath = modelPath.FullName
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});
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codeGenerator.GenerateOutput();
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}
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internal (IDataView, IDataView) LoadData(MLContext context, TextLoader.Options textLoaderOptions)
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{
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logger.Log(LogLevel.Info, Strings.CreateDataLoader);
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var textLoader = context.Data.CreateTextLoader(textLoaderOptions);
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logger.Log(LogLevel.Info, Strings.LoadData);
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var trainData = textLoader.Load(settings.Dataset.FullName);
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var validationData = settings.ValidationDataset == null ? null : textLoader.Load(settings.ValidationDataset.FullName);
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return (trainData, validationData);
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}
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}
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}

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