void New_FileBasedSavingOfData()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
var trainData = pipeline.Fit(new MultiFileSource(dataPath)).Read(new MultiFileSource(dataPath));
using (var file = env.CreateOutputFile("i.idv"))
trainData.SaveAsBinary(env, file.CreateWriteStream());
var trainer = new MySdca(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label");
var loadedTrainData = new BinaryLoader(env, new BinaryLoader.Arguments(), new MultiFileSource("i.idv"));
// Train.
var model = trainer.Train(loadedTrainData);
DeleteOutputPath("i.idv");
}
}
public void New_TrainWithInitialPredictor()
{
var dataPath = GetDataPath(SentimentDataPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
// Train the pipeline, prepare train set.
var reader = pipeline.Fit(new MultiFileSource(dataPath));
var trainData = reader.Read(new MultiFileSource(dataPath));
// Train the first predictor.
var trainer = new MySdca(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
}, "Features", "Label");
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = new MyAveragedPerceptron(env, new AveragedPerceptronTrainer.Arguments(), "Features", "Label");
var finalModel = secondTrainer.Train(trainData, firstModel.InnerModel);
}
}
public void New_ReconfigurablePrediction()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var dataReader = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Fit(new MultiFileSource(dataPath));
var data = dataReader.Read(new MultiFileSource(dataPath));
var testData = dataReader.Read(new MultiFileSource(testDataPath));
// Pipeline.
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs())
.Fit(data);
var trainer = new MySdca(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label");
var trainData = pipeline.Transform(data);
var model = trainer.Fit(trainData);
var scoredTest = model.Transform(pipeline.Transform(testData));
var metrics = new MyBinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments()).Evaluate(scoredTest, "Label", "Probability");
var newModel = model.Clone(new BinaryClassifierScorer.Arguments {
Threshold = 0.01f, ThresholdColumn = DefaultColumnNames.Probability
});
var newScoredTest = newModel.Transform(pipeline.Transform(testData));
var newMetrics = new MyBinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments {
Threshold = 0.01f, UseRawScoreThreshold = false
}).Evaluate(newScoredTest, "Label", "Probability");
}
}
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 MySdca(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);
}
}
public void New_TrainWithValidationSet()
{
var dataPath = GetDataPath(SentimentDataPath);
var validationDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
// Train the pipeline, prepare train and validation set.
var reader = pipeline.Fit(new MultiFileSource(dataPath));
var trainData = reader.Read(new MultiFileSource(dataPath));
var validData = reader.Read(new MultiFileSource(validationDataPath));
// Train model with validation set.
var trainer = new MySdca(env, new Runtime.Learners.LinearClassificationTrainer.Arguments(), "Features", "Label");
var model = trainer.Train(trainData, validData);
}
}
