public void SdcaBinaryClassification()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.breastCancer.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new BinaryClassificationContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadBool(0), features: c.LoadFloat(1, 9)));
LinearBinaryModelParameters pred = null;
ParameterMixingCalibratedPredictor cali = null;
var est = reader.MakeNewEstimator()
.Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features,
maxIterations: 2,
onFit: (p, c) => { pred = p; cali = c; },
advancedSettings: s => s.NumThreads = 1)));
var pipe = reader.Append(est);
Assert.Null(pred);
Assert.Null(cali);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
Assert.NotNull(cali);
// 9 input features, so we ought to have 9 weights.
Assert.Equal(9, pred.Weights.Count);
var data = model.Read(dataSource);
var metrics = ctx.Evaluate(data, r => r.label, r => r.preds);
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.Accuracy, 0, 1);
Assert.InRange(metrics.Auc, 0, 1);
Assert.InRange(metrics.Auprc, 0, 1);
Assert.InRange(metrics.LogLoss, 0, double.PositiveInfinity);
Assert.InRange(metrics.Entropy, 0, double.PositiveInfinity);
// Just output some data on the schema for fun.
var schema = data.AsDynamic.Schema;
for (int c = 0; c < schema.Count; ++c)
{
Console.WriteLine($"{schema[c].Name}, {schema[c].Type}");
}
}
public void SdcaBinaryClassificationNoCalibration()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.breastCancer.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new BinaryClassificationContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadBool(0), features: c.LoadFloat(1, 9)));
LinearBinaryModelParameters pred = null;
var loss = new HingeLoss(1);
// With a custom loss function we no longer get calibrated predictions.
var est = reader.MakeNewEstimator()
.Append(r => (r.label, preds: ctx.Trainers.Sdca(r.label, r.features,
maxIterations: 2,
loss: loss, onFit: p => pred = p,
advancedSettings: s => s.NumThreads = 1)));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
// 9 input features, so we ought to have 9 weights.
Assert.Equal(9, pred.Weights.Count);
var data = model.Read(dataSource);
var metrics = ctx.Evaluate(data, r => r.label, r => r.preds);
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.Accuracy, 0, 1);
Assert.InRange(metrics.Auc, 0, 1);
Assert.InRange(metrics.Auprc, 0, 1);
// Just output some data on the schema for fun.
var schema = data.AsDynamic.Schema;
for (int c = 0; c < schema.ColumnCount; ++c)
{
Console.WriteLine($"{schema.GetColumnName(c)}, {schema.GetColumnType(c)}");
}
}
public void AveragePerceptronCalibration()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.breastCancer.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new BinaryClassificationContext(env);
var reader = TextLoader.CreateReader(env,
c => (label: c.LoadBool(0), features: c.LoadFloat(1, 9)));
LinearBinaryModelParameters pred = null;
var loss = new HingeLoss(1);
var est = reader.MakeNewEstimator()
.Append(r => (r.label, preds: ctx.Trainers.AveragedPerceptron(r.label, r.features, lossFunction: loss,
numIterations: 2, onFit: p => pred = p)));
var pipe = reader.Append(est);
Assert.Null(pred);
var model = pipe.Fit(dataSource);
Assert.NotNull(pred);
// 9 input features, so we ought to have 9 weights.
Assert.Equal(9, pred.Weights.Count);
var data = model.Read(dataSource);
var metrics = ctx.Evaluate(data, r => r.label, r => r.preds);
// Run a sanity check against a few of the metrics.
Assert.InRange(metrics.Accuracy, 0, 1);
Assert.InRange(metrics.Auc, 0, 1);
Assert.InRange(metrics.Auprc, 0, 1);
}
public static float[] GetLinearModelWeights(LinearBinaryModelParameters linearModel)
{
return(linearModel.Weights.ToArray());
}
