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 TrainSaveModelAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
PredictionEngine <SentimentData, SentimentPrediction> model;
using (var file = env.CreateTempFile())
{
// Save model.
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
using (var ch = env.Start("saving"))
TrainUtils.SaveModel(env, ch, file, predictor, scoreRoles);
// Load model.
using (var fs = file.OpenReadStream())
model = env.CreatePredictionEngine <SentimentData, SentimentPrediction>(fs);
}
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath)));
var testData = testLoader.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = model.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);
}
}
}
void Extensibility()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), 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 lambda = LambdaTransform.CreateMap(env, loader, action);
var term = new TermTransform(env, lambda, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
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 = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
public void Evaluation()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Create prediction engine and test predictions.
var model = env.CreatePredictionEngine <SentimentData, SentimentPrediction>(scorer);
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath)));
var testData = testLoader.AsEnumerable <SentimentData>(env, false);
var dataEval = new RoleMappedData(scorer, label: "Label", feature: "Features", opt: true);
var evaluator = new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments()
{
});
var metricsDict = evaluator.Evaluate(dataEval);
var metrics = BinaryClassificationMetrics.FromMetrics(env, metricsDict["OverallMetrics"], metricsDict["ConfusionMatrix"])[0];
}
}
void MultithreadedPrediction()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Create prediction engine and test predictions.
var model = env.CreatePredictionEngine <SentimentData, SentimentPrediction>(scorer);
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath)));
var testData = testLoader.AsEnumerable <SentimentData>(env, false);
Parallel.ForEach(testData, (input) =>
{
lock (model)
{
var prediction = model.Predict(input);
}
});
}
}
public void SimpleTrainAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline
var loader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(dataPath));
var trans = TextTransform.Create(env, MakeSentimentTextTransformArgs(), loader);
// Train
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
});
var cached = new CacheDataView(env, trans, prefetch: null);
var trainRoles = new RoleMappedData(cached, label: "Label", feature: "Features");
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(trans, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Create prediction engine and test predictions.
var model = env.CreatePredictionEngine <SentimentData, SentimentPrediction>(scorer);
// Take a couple examples out of the test data and run predictions on top.
var testLoader = new TextLoader(env, MakeSentimentTextLoaderArgs(), new MultiFileSource(GetDataPath(SentimentTestPath)));
var testData = testLoader.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = model.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);
}
}
}
void Metacomponents()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new Ova(env, new Ova.Arguments
{
PredictorType = new SimpleComponentFactory <ITrainer <IPredictorProducing <float> > >
(
(e) => new FastTreeBinaryClassificationTrainer(e, new FastTreeBinaryClassificationTrainer.Arguments())
)
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
var keyToValue = new KeyToValueTransform(env, scorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisData, IrisPrediction>(keyToValue);
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 = model.Predict(input);
Assert.True(prediction.PredictedLabel == input.Label);
}
}
}
void DecomposableTrainAndPredict()
{
var dataPath = GetDataPath(IrisDataPath);
using (var env = new TlcEnvironment())
{
var loader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var term = new TermTransform(env, loader, "Label");
var concat = new ConcatTransform(env, term, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth");
var trainer = new SdcaMultiClassTrainer(env, new SdcaMultiClassTrainer.Arguments {
MaxIterations = 100, Shuffle = true, NumThreads = 1
});
IDataView trainData = trainer.Info.WantCaching ? (IDataView) new CacheDataView(env, concat, prefetch: null) : concat;
var trainRoles = new RoleMappedData(trainData, label: "Label", feature: "Features");
// Auto-normalization.
NormalizeTransform.CreateIfNeeded(env, ref trainRoles, trainer);
var predictor = trainer.Train(new Runtime.TrainContext(trainRoles));
var scoreRoles = new RoleMappedData(concat, label: "Label", feature: "Features");
IDataScorerTransform scorer = ScoreUtils.GetScorer(predictor, scoreRoles, env, trainRoles.Schema);
// Cut out term transform from pipeline.
var newScorer = ApplyTransformUtils.ApplyAllTransformsToData(env, scorer, loader, term);
var keyToValue = new KeyToValueTransform(env, newScorer, "PredictedLabel");
var model = env.CreatePredictionEngine <IrisDataNoLabel, IrisPrediction>(keyToValue);
var testLoader = new TextLoader(env, MakeIrisTextLoaderArgs(), new MultiFileSource(dataPath));
var testData = testLoader.AsEnumerable <IrisDataNoLabel>(env, false);
foreach (var input in testData.Take(20))
{
var prediction = model.Predict(input);
Assert.True(prediction.PredictedLabel == "Iris-setosa");
}
}
}
