public void TestLogisticRegressionWithStats()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
pipe = pipe.Append(ML.BinaryClassification.Trainers.LogisticRegression(
new LogisticRegression.Options
{
ShowTrainingStats = true,
StdComputer = new ComputeLRTrainingStdThroughHal(),
}));
var transformer = pipe.Fit(dataView) as TransformerChain <BinaryPredictionTransformer <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > >;
var linearModel = transformer.LastTransformer.Model.SubModel as LinearBinaryModelParameters;
var stats = linearModel.Statistics;
LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue);
CompareNumbersWithTolerance(stdError, 0.250672936);
CompareNumbersWithTolerance(zScore, 7.97852373);
var scoredData = transformer.Transform(dataView);
var coeffcients = stats.GetCoefficientStatistics(linearModel, scoredData.Schema["Features"], 100);
Assert.Equal(19, coeffcients.Length);
foreach (var coefficient in coeffcients)
{
Assert.True(coefficient.StandardError < 1.0);
}
}
public void TestLogisticRegressionNoStats()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
pipe = pipe.Append(new LogisticRegression(Env, "Label", "Features", advancedSettings: s => { s.ShowTrainingStats = true; }));
var transformerChain = pipe.Fit(dataView) as TransformerChain <BinaryPredictionTransformer <ParameterMixingCalibratedPredictor> >;
var linearModel = transformerChain.LastTransformer.Model.SubPredictor as LinearBinaryModelParameters;
var stats = linearModel.Statistics;
LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue);
Assert.Equal(0f, stdError);
Assert.Equal(0f, zScore);
}
public void TestLogisticRegressionNoStats()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
pipe = pipe.Append(ML.BinaryClassification.Trainers.LogisticRegression(new LogisticRegression.Options {
ShowTrainingStats = true
}));
var transformerChain = pipe.Fit(dataView) as TransformerChain <BinaryPredictionTransformer <CalibratedModelParametersBase <LinearBinaryModelParameters, PlattCalibrator> > >;
var linearModel = transformerChain.LastTransformer.Model.SubModel as LinearBinaryModelParameters;
var stats = linearModel.Statistics;
LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue);
Assert.Equal(0f, stdError);
Assert.Equal(0f, zScore);
}
public void TestLogisticRegressionStats_MKL()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetBinaryClassificationPipeline();
pipe = pipe.Append(new LogisticRegression(Env, "Label", "Features", advancedSettings: s => {
s.ShowTrainingStats = true;
s.StdComputer = new ComputeLRTrainingStdThroughHal();
}));
var transformerChain = pipe.Fit(dataView) as TransformerChain <BinaryPredictionTransformer <ParameterMixingCalibratedPredictor> >;
var linearModel = transformerChain.LastTransformer.Model.SubPredictor as LinearBinaryPredictor;
var stats = linearModel.Statistics;
LinearModelStatistics.TryGetBiasStatistics(stats, 2, out float stdError, out float zScore, out float pValue);
CompareNumbersWithTolerance(stdError, 0.250672936);
CompareNumbersWithTolerance(zScore, 7.97852373);
}
