public void SetupPredictBenchmarks()
{
_trainedModel = Train(_dataPath);
_consumer.Consume(_trainedModel.Predict(_example));
var testData = new Legacy.Data.TextLoader(_dataPath).CreateFrom <IrisData>(useHeader: true);
var evaluator = new ClassificationEvaluator();
_metrics = evaluator.Evaluate(_trainedModel, testData);
_batches = new IrisData[_batchSizes.Length][];
for (int i = 0; i < _batches.Length; i++)
{
var batch = new IrisData[_batchSizes[i]];
_batches[i] = batch;
for (int bi = 0; bi < batch.Length; bi++)
{
batch[bi] = _example;
}
}
}
public void TrainAndPredictIrisModelTest()
{
string dataPath = GetDataPath("iris.txt");
var pipeline = new Legacy.LearningPipeline(seed: 1, conc: 1);
pipeline.Add(new TextLoader(dataPath).CreateFrom <IrisData>(useHeader: false));
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
"SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
Legacy.PredictionModel <IrisData, IrisPrediction> model = pipeline.Train <IrisData, IrisPrediction>();
IrisPrediction prediction = model.Predict(new IrisData()
{
SepalLength = 5.1f,
SepalWidth = 3.3f,
PetalLength = 1.6f,
PetalWidth = 0.2f,
});
Assert.Equal(1, prediction.PredictedLabels[0], 2);
Assert.Equal(0, prediction.PredictedLabels[1], 2);
Assert.Equal(0, prediction.PredictedLabels[2], 2);
prediction = model.Predict(new IrisData()
{
SepalLength = 6.4f,
SepalWidth = 3.1f,
PetalLength = 5.5f,
PetalWidth = 2.2f,
});
Assert.Equal(0, prediction.PredictedLabels[0], 2);
Assert.Equal(0, prediction.PredictedLabels[1], 2);
Assert.Equal(1, prediction.PredictedLabels[2], 2);
prediction = model.Predict(new IrisData()
{
SepalLength = 4.4f,
SepalWidth = 3.1f,
PetalLength = 2.5f,
PetalWidth = 1.2f,
});
Assert.Equal(.2, prediction.PredictedLabels[0], 1);
Assert.Equal(.8, prediction.PredictedLabels[1], 1);
Assert.Equal(0, prediction.PredictedLabels[2], 2);
// Note: Testing against the same data set as a simple way to test evaluation.
// This isn't appropriate in real-world scenarios.
string testDataPath = GetDataPath("iris.txt");
var testData = new TextLoader(testDataPath).CreateFrom <IrisData>(useHeader: false);
var evaluator = new ClassificationEvaluator();
evaluator.OutputTopKAcc = 3;
ClassificationMetrics metrics = evaluator.Evaluate(model, testData);
Assert.Equal(.98, metrics.AccuracyMacro);
Assert.Equal(.98, metrics.AccuracyMicro, 2);
Assert.Equal(.06, metrics.LogLoss, 2);
Assert.InRange(metrics.LogLossReduction, 94, 96);
Assert.Equal(1, metrics.TopKAccuracy);
Assert.Equal(3, metrics.PerClassLogLoss.Length);
Assert.Equal(0, metrics.PerClassLogLoss[0], 1);
Assert.Equal(.1, metrics.PerClassLogLoss[1], 1);
Assert.Equal(.1, metrics.PerClassLogLoss[2], 1);
ConfusionMatrix matrix = metrics.ConfusionMatrix;
Assert.Equal(3, matrix.Order);
Assert.Equal(3, matrix.ClassNames.Count);
Assert.Equal("0", matrix.ClassNames[0]);
Assert.Equal("1", matrix.ClassNames[1]);
Assert.Equal("2", matrix.ClassNames[2]);
Assert.Equal(50, matrix[0, 0]);
Assert.Equal(50, matrix["0", "0"]);
Assert.Equal(0, matrix[0, 1]);
Assert.Equal(0, matrix["0", "1"]);
Assert.Equal(0, matrix[0, 2]);
Assert.Equal(0, matrix["0", "2"]);
Assert.Equal(0, matrix[1, 0]);
Assert.Equal(0, matrix["1", "0"]);
Assert.Equal(48, matrix[1, 1]);
Assert.Equal(48, matrix["1", "1"]);
Assert.Equal(2, matrix[1, 2]);
Assert.Equal(2, matrix["1", "2"]);
Assert.Equal(0, matrix[2, 0]);
Assert.Equal(0, matrix["2", "0"]);
Assert.Equal(1, matrix[2, 1]);
Assert.Equal(1, matrix["2", "1"]);
Assert.Equal(49, matrix[2, 2]);
Assert.Equal(49, matrix["2", "2"]);
}
