RecommendationExperiment Класс
Определение
Важно!
Некоторые сведения относятся к предварительной версии продукта, в которую до выпуска могут быть внесены существенные изменения. Майкрософт не предоставляет никаких гарантий, явных или подразумеваемых, относительно приведенных здесь сведений.
Эксперимент AutoML с наборами данных рекомендаций.
public sealed class RecommendationExperiment : Microsoft.ML.AutoML.ExperimentBase<Microsoft.ML.Data.RegressionMetrics,Microsoft.ML.AutoML.RecommendationExperimentSettings>type RecommendationExperiment = class
inherit ExperimentBase<RegressionMetrics, RecommendationExperimentSettings>Public NotInheritable Class RecommendationExperiment
Inherits ExperimentBase(Of RegressionMetrics, RecommendationExperimentSettings)- Наследование
Примеры
// Licensed to the .NET Foundation under one or more agreements.
// The .NET Foundation licenses this file to you under the MIT license.
// See the LICENSE file in the project root for more information.
using System;
using System.IO;
using System.Linq;
using Microsoft.ML.AutoML.Samples.DataStructures;
using Microsoft.ML.Data;
namespace Microsoft.ML.AutoML.Samples
{
public static class RecommendationExperiment
{
private static string TrainDataPath = "<Path to your train dataset goes here>";
private static string TestDataPath = "<Path to your test dataset goes here>";
private static string ModelPath = @"<Desired model output directory goes here>\Model.zip";
private static string LabelColumnName = "Rating";
private static string UserColumnName = "UserId";
private static string ItemColumnName = "MovieId";
private static uint ExperimentTime = 60;
public static void Run()
{
MLContext mlContext = new MLContext();
// STEP 1: Load data
IDataView trainDataView = mlContext.Data.LoadFromTextFile<Movie>(TrainDataPath, hasHeader: true, separatorChar: ',');
IDataView testDataView = mlContext.Data.LoadFromTextFile<Movie>(TestDataPath, hasHeader: true, separatorChar: ',');
// STEP 2: Run AutoML experiment
Console.WriteLine($"Running AutoML recommendation experiment for {ExperimentTime} seconds...");
ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto()
.CreateRecommendationExperiment(new RecommendationExperimentSettings() { MaxExperimentTimeInSeconds = ExperimentTime })
.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = LabelColumnName,
UserIdColumnName = UserColumnName,
ItemIdColumnName = ItemColumnName
});
// STEP 3: Print metric from best model
RunDetail<RegressionMetrics> bestRun = experimentResult.BestRun;
Console.WriteLine($"Total models produced: {experimentResult.RunDetails.Count()}");
Console.WriteLine($"Best model's trainer: {bestRun.TrainerName}");
Console.WriteLine($"Metrics of best model from validation data --");
PrintMetrics(bestRun.ValidationMetrics);
// STEP 5: Evaluate test data
IDataView testDataViewWithBestScore = bestRun.Model.Transform(testDataView);
RegressionMetrics testMetrics = mlContext.Recommendation().Evaluate(testDataViewWithBestScore, labelColumnName: LabelColumnName);
Console.WriteLine($"Metrics of best model on test data --");
PrintMetrics(testMetrics);
// STEP 6: Save the best model for later deployment and inferencing
mlContext.Model.Save(bestRun.Model, trainDataView.Schema, ModelPath);
// STEP 7: Create prediction engine from the best trained model
var predictionEngine = mlContext.Model.CreatePredictionEngine<Movie, MovieRatingPrediction>(bestRun.Model);
// STEP 8: Initialize a new test, and get the prediction
var testMovie = new Movie
{
UserId = "1",
MovieId = "1097",
};
var prediction = predictionEngine.Predict(testMovie);
Console.WriteLine($"Predicted rating for: {prediction.Rating}");
// Only predict for existing users
testMovie = new Movie
{
UserId = "612", // new user
MovieId = "2940"
};
prediction = predictionEngine.Predict(testMovie);
Console.WriteLine($"Expected Rating NaN for unknown user, Predicted: {prediction.Rating}");
Console.WriteLine("Press any key to continue...");
Console.ReadKey();
}
private static void PrintMetrics(RegressionMetrics metrics)
{
Console.WriteLine($"MeanAbsoluteError: {metrics.MeanAbsoluteError}");
Console.WriteLine($"MeanSquaredError: {metrics.MeanSquaredError}");
Console.WriteLine($"RootMeanSquaredError: {metrics.RootMeanSquaredError}");
Console.WriteLine($"RSquared: {metrics.RSquared}");
}
}
}