public void TestEstimatorSymSgdInitPredictor()
{
(var pipe, var dataView) = GetBinaryClassificationPipeline();
var transformedData = pipe.Fit(dataView).Transform(dataView);
var initPredictor = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscent().Fit(transformedData);
var data = initPredictor.Transform(transformedData);
var withInitPredictor = new SymSgdClassificationTrainer(Env, new SymSgdClassificationTrainer.Options()).Fit(transformedData,
modelParameters: initPredictor.Model.SubModel);
var outInitData = withInitPredictor.Transform(transformedData);
var notInitPredictor = new SymSgdClassificationTrainer(Env, new SymSgdClassificationTrainer.Options()).Fit(transformedData);
var outNoInitData = notInitPredictor.Transform(transformedData);
int numExamples = 10;
var col1 = data.GetColumn <float>(data.Schema["Score"]).Take(numExamples).ToArray();
var col2 = outInitData.GetColumn <float>(outInitData.Schema["Score"]).Take(numExamples).ToArray();
var col3 = outNoInitData.GetColumn <float>(outNoInitData.Schema["Score"]).Take(numExamples).ToArray();
bool col12Diff = default;
bool col23Diff = default;
bool col13Diff = default;
for (int i = 0; i < numExamples; i++)
{
col12Diff = col12Diff || (col1[i] != col2[i]);
col23Diff = col23Diff || (col2[i] != col3[i]);
col13Diff = col13Diff || (col1[i] != col3[i]);
}
Contracts.Assert(col12Diff && col23Diff && col13Diff);
Done();
}
public void TestEstimatorSymSgdInitPredictor()
{
(var pipe, var dataView) = GetBinaryClassificationPipeline();
var transformedData = pipe.Fit(dataView).Transform(dataView);
var initPredictor = new LinearClassificationTrainer(Env, "Features", "Label").Fit(transformedData);
var data = initPredictor.Transform(transformedData);
var withInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData, initialPredictor: initPredictor.Model);
var outInitData = withInitPredictor.Transform(transformedData);
var notInitPredictor = new SymSgdClassificationTrainer(Env, "Features", "Label").Train(transformedData);
var outNoInitData = notInitPredictor.Transform(transformedData);
int numExamples = 10;
var col1 = data.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
var col2 = outInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
var col3 = outNoInitData.GetColumn <float>(Env, "Score").Take(numExamples).ToArray();
bool col12Diff = default;
bool col23Diff = default;
bool col13Diff = default;
for (int i = 0; i < numExamples; i++)
{
col12Diff = col12Diff || (col1[i] != col2[i]);
col23Diff = col23Diff || (col2[i] != col3[i]);
col13Diff = col13Diff || (col1[i] != col3[i]);
}
Contracts.Assert(col12Diff && col23Diff && col13Diff);
Done();
}
public void TestEstimatorSymSgdClassificationTrainer()
{
(var pipe, var dataView) = GetBinaryClassificationPipeline();
var trainer = new SymSgdClassificationTrainer(Env, new SymSgdClassificationTrainer.Options());
var pipeWithTrainer = pipe.Append(trainer);
TestEstimatorCore(pipeWithTrainer, dataView);
var transformedDataView = pipe.Fit(dataView).Transform(dataView);
var model = trainer.Fit(transformedDataView);
trainer.Fit(transformedDataView, model.Model.SubModel);
Done();
}
