public async Task CreateAsyncAddsEntity()
{
var dbContext = ApplicationDbContextInMemoryFactory.InitializeContext();
var recommendationsRepository = new EfDeletableEntityRepository <Recommendation>(dbContext);
var service = new RecommendationsService(recommendationsRepository);
await service.CreateAsync("TestUser1", "TestUser8", "Recommendation");
Assert.Equal(1, recommendationsRepository.All().Count());
}
public static BrowseRecommendationsResults Run(Configuration config = null)
{
if (config == null)
{
Config = new Configuration();
}
else
{
Config = config;
}
var stopwatch = System.Diagnostics.Stopwatch.StartNew();
results.Add($"Building test {Config.TestNumber} directories...");
if (!Directory.Exists(Configuration.BaseDirectory))
{
Directory.CreateDirectory(Configuration.BaseDirectory);
}
if (!Directory.Exists($"{Configuration.BaseDirectory}/{Config.TestNumber}"))
{
Directory.CreateDirectory($"{Configuration.BaseDirectory}/{Config.TestNumber}");
}
if (!Directory.Exists($"{Configuration.BaseDirectory}/dependencies"))
{
Directory.CreateDirectory($"{Configuration.BaseDirectory}/dependencies");
}
if (!File.Exists(Config.AgentsFile))
{
results.Add("Generating agents file...");
Generators.GenerateAgentsFile(Config);
}
if (!File.Exists(Config.SitesFile))
{
results.Add("Generating sites file...");
Generators.GenerateSitesFile(Config);
}
if (!File.Exists(Config.InputFilePref) || !File.Exists(Config.InputFileRand))
{
results.Add("Generating browse history files...");
Generators.GenerateNewBrowseFiles(Config);
}
var typesToProcess = new[] { "pref", "rand" };
foreach (var typeToProcess in typesToProcess)
{
Config.CurrentType = typeToProcess;
results.Add($"Initializing {Config.CurrentType}...");
results.Add("Extracting test file...");
if (!File.Exists(Config.TestFile))
{
//build test file from input
var lines = File.ReadAllLines(Config.InputFile);
var numberForTest = (lines.Length * Config.PercentOfDataIsTest);
var linesToRemove = new List <int>();
using (StreamWriter w = File.AppendText(Config.TestFile))
{
w.WriteLine("user_id,item_id,rating,timestamp,iteration".ToLower());
int recordsCopied = 0;
while (recordsCopied < numberForTest)
{
var r = new Random();
var randomLineNumber = r.Next(1, lines.Length - 1);
while (linesToRemove.Contains(randomLineNumber))
{
randomLineNumber = r.Next(1, lines.Length - 1);
}
var line = lines[randomLineNumber];
w.WriteLine(line);
linesToRemove.Add(randomLineNumber);
recordsCopied++;
}
}
//remove test data from input file
if (File.Exists(Config.InputFile + ".backup"))
{
File.Delete(Config.InputFile + ".backup");
}
File.Move(Config.InputFile, Config.InputFile + ".backup");
using (StreamWriter w = File.AppendText(Config.InputFile))
{
w.WriteLine("user_id,item_id,rating,timestamp,iteration".ToLower());
int i = -1;
foreach (var line in lines)
{
i++;
if (i == 0 || linesToRemove.Contains(i))
{
continue;
}
w.WriteLine(line);
}
}
}
MLContext mlContext = new MLContext();
(IDataView trainingDataView, IDataView testDataView) = LoadData(mlContext);
Agents = new List <Agent>();
using (var fileStream = File.OpenRead(Config.AgentsFile))
{
using (var streamReader = new StreamReader(fileStream, Encoding.UTF8, true, 128))
{
var i = -1;
String line;
while ((line = streamReader.ReadLine()) != null)
{
i++;
if (i == 0)
{
continue;
}
var o = line.Split(Convert.ToChar(","));
Agents.Add(new Agent(Convert.ToInt32(o[0]), o[1], Convert.ToInt32(Convert.ToDouble(o[2]))));
}
}
}
Sites = new List <Site>();
using (var fileStream = File.OpenRead(Config.SitesFile))
{
using (var streamReader = new StreamReader(fileStream, Encoding.UTF8, true, 128))
{
String line;
while ((line = streamReader.ReadLine()) != null)
{
var o = line.Split(Convert.ToChar(","));
try
{
Sites.Add(new Site(Convert.ToInt32(o[0]), o[1]));
}
catch { } //lazy, don't @ me
}
}
}
results.Add($"Initializing model and associated requirements...");
if (!File.Exists(Config.ModelFile))
{
ITransformer model = BuildAndTrainModel(mlContext, trainingDataView);
EvaluateModel(mlContext, testDataView, model);
UseModelForSinglePrediction(mlContext, model);
SaveModel(mlContext, trainingDataView.Schema, model);
}
/*
* results.Add("=============== Running Experiment ===============");
* var experimentSettings = new RecommendationExperimentSettings();
* experimentSettings.MaxExperimentTimeInSeconds = 3600;
* experimentSettings.OptimizingMetric = RegressionMetric.MeanSquaredError;
* var experiment = mlContext.Auto().CreateRecommendationExperiment(experimentSettings);
* ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto()
* .CreateRecommendationExperiment(new RecommendationExperimentSettings() { MaxExperimentTimeInSeconds = 3600 })
* .Execute(trainingDataView, testDataView,
* new ColumnInformation()
* {
* LabelColumnName = "Label",
* UserIdColumnName = "userId",
* ItemIdColumnName = "itemId"
* });
* // STEP 3: Print metric from best model
* RunDetail<RegressionMetrics> bestRun = experimentResult.BestRun;
* results.Add($"Total models produced: {experimentResult.RunDetails.Count()}");
* results.Add($"Best model's trainer: {bestRun.TrainerName}");
* results.Add($"Metrics of best model from validation data --");
* PrintMetrics(bestRun.ValidationMetrics);
* Environment.Exit(1);
*/
//now that we have a model, we'll loop through that model x times - same model, growing dataset over iteration
for (var i = 1; i < Config.Iterations; i++)
{
Config.CurrentIteration = i;
//Define DataViewSchema for data preparation pipeline and trained model
DataViewSchema modelSchema;
// Load trained model
var trainedModel = mlContext.Model.Load(Config.ModelFile, out modelSchema);
// Load data preparation pipeline and trained model
UseModelForSinglePrediction(mlContext, trainedModel);
}
results.Add("Generating final reports...");
Generators.GenerateReportFile(Config);
results.Add($"{Config.CurrentType} completed in {stopwatch.ElapsedMilliseconds} ms");
}
stopwatch.Stop();
results.Add($"Test completed in {stopwatch.ElapsedMilliseconds} ms");
//load result file
var recommendations = RecommendationsService.Load(config.ResultFileOut);
var browseRecommendationsResults = new BrowseRecommendationsResults {
JobOutput = results, Recommendations = recommendations
};
return(browseRecommendationsResults);
}
