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");
}
}