RecommendationExperiment Třída
Definice
Důležité
Některé informace platí pro předběžně vydaný produkt, který se může zásadně změnit, než ho výrobce nebo autor vydá. Microsoft neposkytuje žádné záruky, výslovné ani předpokládané, týkající se zde uváděných informací.
Experiment AutoML s datovými sadami doporučení
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)- Dědičnost
Příklady
// 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}");
}
}
}