public void New_SimpleTrainAndPredict()
{
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()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model.Transformer);
// Take a couple examples out of the test data and run predictions on top.
var testData = model.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_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 LinearClassificationTrainer(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 = new BinaryPredictionTransformer <IPredictorProducing <float> >(env, model.Model, trainData.Schema, model.FeatureColumn, 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_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 LinearClassificationTrainer(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.Model);
}
}
public void New_Evaluation()
{
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()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Evaluate on the test set.
var dataEval = model.Read(new MultiFileSource(testDataPath));
var evaluator = new MyBinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments()
{
});
var metrics = evaluator.Evaluate(dataEval);
}
}
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");
}
}
void New_MultithreadedPrediction()
{
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()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model.Transformer);
// Take a couple examples out of the test data and run predictions on top.
var testData = model.Reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
Parallel.ForEach(testData, (input) =>
{
lock (engine)
{
var prediction = engine.Predict(input);
}
});
}
}
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 MySdcaMulticlass(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);
}
}
}
void New_CrossValidation()
{
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));
// Pipeline.
var pipeline = new MyTextTransform(env, MakeSentimentTextTransformArgs())
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1,
ConvergenceTolerance = 1f
}, "Features", "Label"));
var cv = new MyCrossValidation.BinaryCrossValidator(env)
{
NumFolds = 2
};
var cvResult = cv.CrossValidate(data, pipeline);
}
}
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);
}
}
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);
}
}
void New_Visibility()
{
var dataPath = GetDataPath(SentimentDataPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
var data = pipeline.FitAndRead(new MultiFileSource(dataPath));
// In order to find out available column names, you can go through schema and check
// column names and appropriate type for getter.
for (int i = 0; i < data.Schema.ColumnCount; i++)
{
var columnName = data.Schema.GetColumnName(i);
var columnType = data.Schema.GetColumnType(i).RawType;
}
using (var cursor = data.GetRowCursor(x => true))
{
Assert.True(cursor.Schema.TryGetColumnIndex("SentimentText", out int textColumn));
Assert.True(cursor.Schema.TryGetColumnIndex("Features_TransformedText", out int transformedTextColumn));
Assert.True(cursor.Schema.TryGetColumnIndex("Features", out int featureColumn));
var originalTextGettter = cursor.GetGetter <DvText>(textColumn);
var transformedTextGettter = cursor.GetGetter <VBuffer <DvText> >(transformedTextColumn);
var featureGettter = cursor.GetGetter <VBuffer <float> >(featureColumn);
DvText text = default;
VBuffer <DvText> transformedText = default;
VBuffer <float> features = default;
while (cursor.MoveNext())
{
originalTextGettter(ref text);
transformedTextGettter(ref transformedText);
featureGettter(ref features);
}
}
}
}
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);
}
}
