void New_DecomposableTrainAndPredict()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var data = new MyTextLoader(env, MakeIrisTextLoaderArgs())
.FitAndRead(new MultiFileSource(dataPath));
var pipeline = new MyConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Append(new MyTermTransform(env, "Label"), TransformerScope.TrainTest)
.Append(new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
}, "Features", "Label"))
.Append(new MyKeyToValueTransform(env, "PredictedLabel"));
var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring);
var engine = new MyPredictionEngine <IrisDataNoLabel, IrisPrediction>(env, model);
var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = engine.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
public void New_Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var data = new MyTextLoader(env, MakeIrisTextLoaderArgs())
.FitAndRead(new MultiFileSource(dataPath));
var sdcaTrainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
}, "Features", "Label");
var pipeline = new MyConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Append(new MyTermTransform(env, "Label"), TransformerScope.TrainTest)
.Append(new MyOva(env, sdcaTrainer))
.Append(new MyKeyToValueTransform(env, "PredictedLabel"));
var model = pipeline.Fit(data);
}
}
void New_Extensibility()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var data = new TextLoader(env, MakeIrisTextLoaderArgs())
.Read(new MultiFileSource(dataPath));
Action <IrisData, IrisData> action = (i, j) =>
{
j.Label = i.Label;
j.PetalLength = i.SepalLength > 3 ? i.PetalLength : i.SepalLength;
j.PetalWidth = i.PetalWidth;
j.SepalLength = i.SepalLength;
j.SepalWidth = i.SepalWidth;
};
var pipeline = new MyConcatTransform(env, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
.Append(new MyLambdaTransform <IrisData, IrisData>(env, action), TransformerScope.TrainTest)
.Append(new TermEstimator(env, "Label"), TransformerScope.TrainTest)
.Append(new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
}, "Features", "Label"))
.Append(new KeyToValueEstimator(env, "PredictedLabel"));
var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring);
var engine = model.MakePredictionFunction <IrisDataNoLabel, IrisPrediction>(env);
var testLoader = TextLoader.ReadFile(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisData>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = engine.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}