[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // This test is being fixed as part of issue #1441.
public void MatrixFactorizationSimpleTrainAndPredict()
{
var mlContext = new MLContext(seed: 1, conc: 1);
// Specific column names of the considered data set
string labelColumnName = "Label";
string userColumnName = "User";
string itemColumnName = "Item";
string scoreColumnName = "Score";
// Create reader for both of training and test data sets
var reader = new TextLoader(mlContext, GetLoaderArgs(labelColumnName, userColumnName, itemColumnName));
// Read training data as an IDataView object
var data = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.trainFilename)));
// Create a pipeline with a single operator.
var pipeline = new MatrixFactorizationTrainer(mlContext, userColumnName, itemColumnName, labelColumnName,
advancedSettings: s =>
{
s.NumIterations = 3;
s.NumThreads = 1; // To eliminate randomness, # of threads must be 1.
s.K = 7;
});
// Train a matrix factorization model.
var model = pipeline.Fit(data);
// Read the test data set as an IDataView
var testData = reader.Read(new MultiFileSource(GetDataPath(TestDatasets.trivialMatrixFactorization.testFilename)));
// Apply the trained model to the test set
var prediction = model.Transform(testData);
// Get output schema and check its column names
var outputSchema = model.GetOutputSchema(data.Schema);
var expectedOutputNames = new string[] { labelColumnName, userColumnName, itemColumnName, scoreColumnName };
foreach (var(i, col) in outputSchema.GetColumns())
{
Assert.True(col.Name == expectedOutputNames[i]);
}
// Retrieve label column's index from the test IDataView
testData.Schema.TryGetColumnIndex(labelColumnName, out int labelColumnId);
// Retrieve score column's index from the IDataView produced by the trained model
prediction.Schema.TryGetColumnIndex(scoreColumnName, out int scoreColumnId);
// Compute prediction errors
var metrices = mlContext.Regression.Evaluate(prediction, label: labelColumnName, score: scoreColumnName);
// Determine if the selected metric is reasonable for different platforms
double tolerance = Math.Pow(10, -7);
if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
{
// Linux case
var expectedUnixL2Error = 0.616821448679879; // Linux baseline
Assert.InRange(metrices.L2, expectedUnixL2Error - tolerance, expectedUnixL2Error + tolerance);
}
else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
{
// The Mac case is just broken. Should be fixed later. Re-enable when done.
// Mac case
//var expectedMacL2Error = 0.61192207960271; // Mac baseline
//Assert.InRange(metrices.L2, expectedMacL2Error - 5e-3, expectedMacL2Error + 5e-3); // 1e-7 is too small for Mac so we try 1e-5
}
else if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
{
// Windows case
var expectedWindowsL2Error = 0.61528733643754685; // Windows baseline
Assert.InRange(metrices.L2, expectedWindowsL2Error - tolerance, expectedWindowsL2Error + tolerance);
}
var modelWithValidation = pipeline.Train(data, testData);
}
