Regression problem instances for testing feature selection

[gkronber]

r9093 added a provider and a configurable problem instance for testing feature selection

[gkronber]

r9094: formatting

[mkommend]

Reviewing comments:

• Use Linq syntax in FeatureSelectionInstanceProvider.GetDataDescriptors as it is IMHO more readable.
• Make the training and test samples configurable in the FeatureSelection DataDescriptor.
• Additionally the ranges for the input variables and weights should also be configurable.
• Why are the generated input values normally (0,1) and not uniformly distributed?
• Obviously the formula to calculate the sigma for the noise RNG (targetSigma * Math.Sqrt(noiseRatio)) works, but I don't understand why? Is this due to the fact that targetSigma is approximately 1.0?
• The final formula y = f(x,w)+e with the selected variables and the according weights should be displayed somewhere (i.e. problem description, problem data parameter).

[gkronber]

r9217: improved implementation of feature selection problem instances based on the review comments by mkommend.

• Created a PRNG for uniformly distributed values with a specified range [min..max[
• Created a class FeatureSelectionRegressionProblemData with additional informative parameters derived from RegressionProblemData
• fixed typos: shuffeled and varialbe

Replying to mkommend:

Reviewing comments:

• Use Linq syntax in FeatureSelectionInstanceProvider.GetDataDescriptors as it is IMHO more readable.

Fixed in r9217

• Make the training and test samples configurable in the FeatureSelection DataDescriptor.

Fixed in r9217 (default values training: 20% more than number of features, test: 5000)

• Additionally the ranges for the input variables and weights should also be configurable.

Fixed in r9217 by adding IRandom parameters to generate the values. Default values x: Normal(0,1) and weights: Uniform(0,10)

• Why are the generated input values normally (0,1) and not uniformly distributed?
• Obviously the formula to calculate the sigma for the noise RNG (targetSigma * Math.Sqrt(noiseRatio)) works, but I don't understand why? Is this due to the fact that targetSigma is approximately 1.0?

Last two points were discussed personally.

• The final formula y = f(x,w)+e with the selected variables and the according weights should be displayed somewhere (i.e. problem description, problem data parameter).

Added informative parameters for selected features, weights, and best achievable R² in the ProblemData parameter (introduced a new derived class with the additional parameters)

Please review the changes again.

[gkronber]

r9218: fixed build fail

[mkommend]

r9231: Corrected rounding in FeatureSelection.cs for the display of the optimal R² in the problem data description.

Reviewed r9217 and found no other mistake as the one fixed with r9231.