public void CanTrainSupervised()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "cooking");
var args = new SupervisedArgs();
var tuneArgs = new AutotuneArgs();
fastText.Supervised("cooking.train.txt", outPath, args, tuneArgs, true);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be(outPath + ".bin");
AssertLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
var debugArgs = DebugArgs.Load("_train.txt");
AssertSupervisedArgs(args, debugArgs.ExternalArgs);
AssertSupervisedArgs(args, debugArgs.ConvertedArgs);
AssertAutotuneArgs(tuneArgs, debugArgs.ExternalTune);
AssertAutotuneArgs(tuneArgs, debugArgs.ConvertedTune);
debugArgs.ExternalInput.Should().Be("cooking.train.txt");
debugArgs.ConvertedInput.Should().Be("cooking.train.txt");
debugArgs.ExternalOutput.Should().Be(outPath);
debugArgs.ConvertedOutput.Should().Be(outPath);
}
public void CanTrainSupervisedWithProgressCallback()
{
using var fastText = new FastTextWrapper();
string outPath = Path.Combine(_tempDir, "cooking");
int callNum = 0;
var args = new SupervisedArgs
{
TrainProgressCallback = (progress, loss, wst, lr, eta) =>
{
callNum++;
}
};
fastText.Supervised("cooking.train.txt", outPath, args);
callNum.Should().BeGreaterThan(0);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be(outPath + ".bin");
AssertLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanHandleUtf8()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "rus");
fastText.Supervised("data.rus.txt", outPath, new SupervisedArgs());
var labels = fastText.GetLabels();
labels.Length.Should().Be(2);
labels.Should().Contain(new[] { "__label__оператор", "__label__выход" });
var pred = fastText.PredictSingle("Позови оператора");
pred.Probability.Should().BeGreaterThan(0);
pred.Label.Should().Be("__label__оператор");
var sourceWords = File.ReadAllText("data.rus.txt")
.Split(new[] { " ", "\r\n", "\n" }, StringSplitOptions.RemoveEmptyEntries)
.Where(x => !x.StartsWith("__label__"))
.Distinct().ToArray();
var nn = fastText.GetNearestNeighbours("оператор", 2);
nn.Length.Should().Be(2);
sourceWords.Should().Contain(nn.Select(x => x.Label));
foreach (var prediction in nn)
{
prediction.Probability.Should().BeGreaterThan(0);
}
}
private static void Test(FastTextWrapper fastText)
{
var labels = fastText.GetLabels();
var prediction = fastText.PredictSingle("Can I use a larger crockpot than the recipe calls for?");
var predictions = fastText.PredictMultiple("Can I use a larger crockpot than the recipe calls for?", 4);
var vector = fastText.GetSentenceVector("Can I use a larger crockpot than the recipe calls for?");
}
public void CanLoadSupervisedModel()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
fastText.LoadModel(_fixture.FastText.ModelPath);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
AssertLabels(fastText.GetLabels());
}
private static void LoadModel()
{
using (var fastText = new FastTextWrapper())
{
fastText.LoadModel(@"D:\__Models\cooking.bin");
var labels = fastText.GetLabels();
var prediction = fastText.PredictSingle("Can I use a larger crockpot than the recipe calls for?");
var predictions = fastText.PredictMultiple("Can I use a larger crockpot than the recipe calls for?", 4);
var vector = fastText.GetSentenceVector("Can I use a larger crockpot than the recipe calls for?");
}
}
static void Main(string[] args)
{
Log.Logger = new LoggerConfiguration()
.MinimumLevel.Debug()
.WriteTo.Console(theme: ConsoleTheme.None)
.CreateLogger();
var log = Log.ForContext <Program>();
var tempDir = Path.Combine(Path.GetTempPath(), Guid.NewGuid().ToString("N"));
Directory.CreateDirectory(tempDir);
log.Information($"Temp dir: {tempDir}");
string outPath = Path.Combine(tempDir, "cooking.bin");
var fastText = new FastTextWrapper(loggerFactory: new LoggerFactory(new[] { new SerilogLoggerProvider() }));
var ftArgs = FastTextArgs.SupervisedDefaults();
ftArgs.epoch = 15;
ftArgs.lr = 1;
ftArgs.dim = 300;
ftArgs.wordNgrams = 2;
ftArgs.minn = 3;
ftArgs.maxn = 6;
fastText.Supervised("cooking.train.txt", outPath, ftArgs);
try
{
File.Delete("_debug.txt");
}
catch
{
}
var result = fastText.TestInternal("cooking.valid.txt", 1, 0.0f, true);
log.Information($"Results:\n\tPrecision: {result.GlobalMetrics.GetPrecision()}" +
$"\n\tRecall: {result.GlobalMetrics.GetRecall()}" +
$"\n\tF1: {result.GlobalMetrics.GetF1()}");
var curve = result.GetPrecisionRecallCurve();
var(_, debugCurve) = TestResult.LoadDebugResult("_debug.txt", fastText.GetLabels());
string plotPath = PlotCurves(tempDir, new [] { curve, debugCurve });
log.Information($"Precision-Recall plot: {plotPath}");
Console.WriteLine("\nPress any key to exit.");
Console.ReadKey();
Directory.Delete(tempDir, true);
}
public void CanTrainModelWithOldApi()
{
var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "cooking");
fastText.Train("cooking.train.txt", outPath, FastTextArgs.SupervisedDefaults());
CheckLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanTrainSupervisedWithNoLogging()
{
var fastText = new FastTextWrapper();
string outPath = Path.Combine(_tempDir, "cooking");
fastText.Supervised("cooking.train.txt", outPath, FastTextArgs.SupervisedDefaults());
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
CheckLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanUsePretrainedVectorsForSupervisedModel()
{
var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "cooking");
var args = FastTextArgs.SupervisedDefaults();
args.PretrainedVectors = "cooking.unsup.300.vec";
args.dim = 300;
fastText.Supervised("cooking.train.txt", outPath, args);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(300);
CheckLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanQuantizeLoadedSupervisedModel()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
fastText.LoadModel(_fixture.FastText.ModelPath);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
AssertLabels(fastText.GetLabels());
string newPath = Path.Combine(Path.GetDirectoryName(_fixture.FastText.ModelPath), Path.GetFileNameWithoutExtension(_fixture.FastText.ModelPath));
fastText.Quantize();
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be(newPath + ".ftz");
File.Exists(newPath + ".ftz").Should().BeTrue();
File.Exists(newPath + ".vec").Should().BeTrue();
}
public void CanTrainSupervisedWithPretrainedVectors()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
string outPath = Path.Combine(_tempDir, "cooking");
var args = new SupervisedArgs();
args.PretrainedVectors = "cooking.unsup.300.vec";
args.dim = 300;
fastText.Supervised("cooking.train.txt", outPath, args, new AutotuneArgs(), true);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(300);
fastText.ModelPath.Should().Be(outPath + ".bin");
AssertLabels(fastText.GetLabels());
File.Exists(outPath + ".bin").Should().BeTrue();
File.Exists(outPath + ".vec").Should().BeTrue();
}
public void CanTrainSupervisedWithRelativeOutput()
{
using var fastText = new FastTextWrapper(loggerFactory: _loggerFactory);
var args = new SupervisedArgs();
var tuneArgs = new AutotuneArgs();
fastText.Supervised("cooking.train.txt", "cooking", args, tuneArgs, true);
fastText.IsModelReady().Should().BeTrue();
fastText.GetModelDimension().Should().Be(100);
fastText.ModelPath.Should().Be("cooking.bin");
AssertLabels(fastText.GetLabels());
File.Exists("cooking.bin").Should().BeTrue();
File.Exists("cooking.vec").Should().BeTrue();
File.Delete("cooking.bin");
File.Delete("cooking.vec");
}
static void Main(string[] args)
{
Log.Logger = new LoggerConfiguration()
.MinimumLevel.Debug()
.WriteTo.Console(theme: ConsoleTheme.None)
.CreateLogger();
var log = Log.ForContext <Program>();
var tempDir = Path.Combine(Path.GetTempPath(), Guid.NewGuid().ToString("N"));
Directory.CreateDirectory(tempDir);
log.Information($"Temp dir: {tempDir}");
string outPath = Path.Combine(tempDir, "cooking.bin");
var fastText = new FastTextWrapper(loggerFactory: new LoggerFactory(new[] { new SerilogLoggerProvider() }));
AnsiConsole.Progress()
.Start(ctx =>
{
var task = ctx.AddTask("Training");
var ftArgs = new SupervisedArgs
{
epoch = 15,
lr = 1,
dim = 300,
wordNgrams = 2,
minn = 3,
maxn = 6,
verbose = 0,
TrainProgressCallback = (progress, loss, wst, lr, eta) =>
{
task.Value = Math.Ceiling(progress * 100);
task.Description = $"Loss: {loss:N3}, words/thread/sec: {wst}, LR: {lr:N5}, ETA: {eta}";
}
};
fastText.Supervised("cooking.train.txt", outPath, ftArgs);
});
try
{
File.Delete("_debug.txt");
}
catch
{
}
log.Information("Validating model on the test set");
var result = fastText.TestInternal("cooking.valid.txt", 1, 0.0f, true);
log.Information($"Results:\n\tPrecision: {result.GlobalMetrics.GetPrecision()}" +
$"\n\tRecall: {result.GlobalMetrics.GetRecall()}" +
$"\n\tF1: {result.GlobalMetrics.GetF1()}");
var curve = result.GetPrecisionRecallCurve();
var(_, debugCurve) = TestResult.LoadDebugResult("_debug.txt", fastText.GetLabels());
string plotPath = PlotCurves(tempDir, new [] { curve, debugCurve });
log.Information($"Precision-Recall plot: {plotPath}");
Console.WriteLine("\nPress any key to exit.");
Console.ReadKey();
Directory.Delete(tempDir, true);
}