[ConditionalFact(typeof(Environment), nameof(Environment.Is64BitProcess))] // LightGBM is 64-bit only
public void LightGbmBinaryClassification()
{
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)));
IPredictorWithFeatureWeights <float> pred = null;
var est = reader.MakeNewEstimator()
.Append(r => (r.label, preds: ctx.Trainers.LightGbm(r.label, r.features,
numBoostRound: 10,
numLeaves: 5,
learningRate: 0.01,
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.
VBuffer <float> weights = new VBuffer <float>();
pred.GetFeatureWeights(ref weights);
Assert.Equal(9, weights.Length);
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 void HogwildSGDBinaryClassification()
{
var env = new MLContext(seed: 0);
var dataPath = GetDataPath(TestDatasets.breastCancer.trainFilename);
var dataSource = new MultiFileSource(dataPath);
var ctx = new BinaryClassificationContext(env);
var reader = TextLoaderStatic.CreateReader(env,
c => (label: c.LoadBool(0), features: c.LoadFloat(1, 9)));
IPredictorWithFeatureWeights <float> pred = null;
var est = reader.MakeNewEstimator()
.Append(r => (r.label, preds: ctx.Trainers.StochasticGradientDescentClassificationTrainer(r.label, r.features,
l2Weight: 0,
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.
VBuffer <float> weights = new VBuffer <float>();
pred.GetFeatureWeights(ref weights);
Assert.Equal(9, weights.Length);
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);
}