void New_MultithreadedPrediction()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var reader = new TextLoader(env, MakeSentimentTextLoaderArgs());
var data = reader.Read(new MultiFileSource(dataPath));
// Pipeline.
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs())
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(data);
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model);
// Take a couple examples out of the test data and run predictions on top.
var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
Parallel.ForEach(testData, (input) =>
{
lock (engine)
{
var prediction = engine.Predict(input);
}
});
}
}
public void New_SimpleTrainAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var reader = new TextLoader(env, MakeSentimentTextLoaderArgs());
var data = reader.Read(new MultiFileSource(dataPath));
// Pipeline.
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs())
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(data);
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model);
// Take a couple examples out of the test data and run predictions on top.
var testData = reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = engine.Predict(input);
// Verify that predictions match and scores are separated from zero.
Assert.Equal(input.Sentiment, prediction.Sentiment);
Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
}
}
}
public void New_TrainWithValidationSet()
{
var dataPath = GetDataPath(SentimentDataPath);
var validationDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var reader = new TextLoader(env, MakeSentimentTextLoaderArgs());
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs());
// Train the pipeline, prepare train and validation set.
var data = reader.Read(new MultiFileSource(dataPath));
var preprocess = pipeline.Fit(data);
var trainData = preprocess.Transform(data);
var validData = preprocess.Transform(reader.Read(new MultiFileSource(validationDataPath)));
// Train model with validation set.
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments(), "Features", "Label");
var model = trainer.Train(trainData, validData);
}
}
public void New_IntrospectiveTraining()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var data = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.FitAndRead(new MultiFileSource(dataPath));
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs())
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(data);
// Get feature weights.
VBuffer <float> weights = default;
model.LastTransformer.Model.GetFeatureWeights(ref weights);
}
}
