public void SdcaMulticlass()
{
var env = new ConsoleEnvironment(seed: 0);
var dataPath = GetDataPath(TestDatasets.iris.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new MulticlassClassificationContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadText(0), features: c.LoadFloat(1, 4)));
MulticlassLogisticRegressionPredictor pred = null;
var loss = new HingeLoss(new HingeLoss.Arguments()
{
Margin = 1
});
// With a custom loss function we no longer get calibrated predictions.
var est = reader.MakeNewEstimator()
.Append(r => (label: r.label.ToKey(), r.features))
.Append(r => (r.label, preds: ctx.Trainers.Sdca(
r.label,
r.features,
maxIterations: 2,
loss: loss, onFit: p => pred = p)));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
VBuffer <float>[] weights = default;
pred.GetWeights(ref weights, out int n);
Assert.True(n == 3 && n == weights.Length);
foreach (var w in weights)
{
Assert.True(w.Length == 4);
}
var biases = pred.GetBiases();
Assert.True(biases.Count() == 3);
var data = model.Read(dataSource);
// Just output some data on the schema for fun.
var schema = data.AsDynamic.Schema;
for (int c = 0; c < schema.ColumnCount; ++c)
{
Console.WriteLine($"{schema.GetColumnName(c)}, {schema.GetColumnType(c)}");
}
var metrics = ctx.Evaluate(data, r => r.label, r => r.preds, 2);
Assert.True(metrics.LogLoss > 0);
Assert.True(metrics.TopKAccuracy > 0);
}
public void MulticlassLogisticRegression()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.iris.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new MulticlassClassificationContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadText(0), features: c.LoadFloat(1, 4)));
MulticlassLogisticRegressionPredictor pred = null;
// With a custom loss function we no longer get calibrated predictions.
var est = reader.MakeNewEstimator()
.Append(r => (label: r.label.ToKey(), r.features))
.Append(r => (r.label, preds: ctx.Trainers.MultiClassLogisticRegression(
r.label,
r.features, onFit: p => pred = p,
advancedSettings: s => s.NumThreads = 1)));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
VBuffer <float>[] weights = default;
pred.GetWeights(ref weights, out int n);
Assert.True(n == 3 && n == weights.Length);
foreach (var w in weights)
{
Assert.True(w.Length == 4);
}
var data = model.Read(dataSource);
// Just output some data on the schema for fun.
var schema = data.AsDynamic.Schema;
for (int c = 0; c < schema.Count; ++c)
{
Console.WriteLine($"{schema[c].Name}, {schema[c].Type}");
}
var metrics = ctx.Evaluate(data, r => r.label, r => r.preds, 2);
Assert.True(metrics.LogLoss > 0);
Assert.True(metrics.TopKAccuracy > 0);
}
private void TrainAndInspectWeights(string dataPath)
{
// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
// as a catalog of available operations and as the source of randomness.
var mlContext = new MLContext();
// Step one: read the data as an IDataView.
// First, we define the reader: specify the data columns and where to find them in the text file.
var reader = mlContext.Data.TextReader(ctx => (
// The four features of the Iris dataset.
SepalLength: ctx.LoadFloat(0),
SepalWidth: ctx.LoadFloat(1),
PetalLength: ctx.LoadFloat(2),
PetalWidth: ctx.LoadFloat(3),
// Label: kind of iris.
Label: ctx.LoadText(4)
),
// Default separator is tab, but the dataset has comma.
separator: ',');
// Retrieve the training data.
var trainData = reader.Read(dataPath);
// This is the predictor ('weights collection') that we will train.
MulticlassLogisticRegressionPredictor predictor = null;
// And these are the normalizer scales that we will learn.
ImmutableArray <float> normScales;
// Build the training pipeline.
var learningPipeline = reader.MakeNewEstimator()
.Append(r => (
r.Label,
// Concatenate all the features together into one column 'Features'.
Features: r.SepalLength.ConcatWith(r.SepalWidth, r.PetalLength, r.PetalWidth)))
.Append(r => (
r.Label,
// Normalize (rescale) the features to be between -1 and 1.
Features: r.Features.Normalize(
// When the normalizer is trained, the below delegate is going to be called.
// We use it to memorize the scales.
onFit: (scales, offsets) => normScales = scales)))
.Append(r => (
r.Label,
// Train the multi-class SDCA model to predict the label using features.
// Note that the label is a text, so it needs to be converted to key using 'ToKey' estimator.
Predictions: mlContext.MulticlassClassification.Trainers.Sdca(r.Label.ToKey(), r.Features,
// When the model is trained, the below delegate is going to be called.
// We use that to memorize the predictor object.
onFit: p => predictor = p)));
// Train the model. During this call our 'onFit' delegate will be invoked,
// and our 'predictor' will be set.
var model = learningPipeline.Fit(trainData);
// Now we can use 'predictor' to look at the weights.
// 'weights' will be an array of weight vectors, one vector per class.
// Our problem has 3 classes, so numClasses will be 3, and weights will contain
// 3 vectors (of 4 values each).
VBuffer <float>[] weights = null;
predictor.GetWeights(ref weights, out int numClasses);
// Similarly we can also inspect the biases for the 3 classes.
var biases = predictor.GetBiases();
// Inspect the normalizer scales.
Console.WriteLine(string.Join(" ", normScales));
}
