Wrapper FS based on Cuckoo Search algorithm #335
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Dear Adam, Is there anyway to get a list or an array of the feature values from the 'selectedFeatureDataset' ?, such as getting the array of values of a certain feature based on the index. That this I need in order to compute functions like Pearson Correlation Coefficient between two features for some purposes |
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Not easily from the selected feature dataset as it's stored row wise. The current information theoretic feature selection dataset converts the data into a columnar storage format before running the feature selection procedure. Depending on what you want to do it might be more efficient to do that, like if you need to compute the correlations between all features. Otherwise you pay a |
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I want to compute the correlation between the entire features in the selected feature dataset and how can I construct the d*n matrix using tribuo engine |
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Thanks for the PR, sorry it took a while to review. The implementation looks interesting, but there's a bit of work required to get it to play nicely with Tribuo's provenance and configuration systems. Also could you add a basic unit test which runs a feature selection operation, similar to the other tests in the FeatureSelection subproject?
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| package FS_Wrapper_Approaches.Discreeting; | |||
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The base package name needs updating to org.tribuo.classification.fs.wrapper and then the files should be moved into the right directory. At the moment this won't compile because the directory and package names don't line up.
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Also all the files need the copyright and license header.
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| package FS_Wrapper_Approaches.Discreeting; | |||
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| import static org.apache.commons.math3.special.Erf.erf; | |||
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Tribuo no longer depends on Apache Commons Math 3, and this import isn't used.
| * Enumeration that contains the types of transfer functions in which they are used to define the type of transfer function | ||
| */ | ||
| public enum TransferFunction { | ||
| V1, V2, V3, V4, S1, S2, S3, S4 |
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The implementation of discreteValue could be folded in here, making the enum contain a DoubleUnaryFunction which performs the necessary operation, and then it could have a discretise method which applies the function the output. Then the Binarizing interface can be removed.
| import org.tribuo.classification.Label; | ||
| import org.tribuo.classification.evaluation.LabelEvaluation; | ||
| import org.tribuo.classification.evaluation.LabelEvaluator; | ||
| import org.tribuo.common.nearest.KNNClassifierOptions; |
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We try to avoid surfacing the options classes in the library code as they are not under the same compatibility guarantees as the library code. It's probably better to import KNNTrainer directly.
| /** | ||
| * This interface includes the evaluation function of each solution | ||
| */ | ||
| public interface FitnessFunction { |
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This interface only contains static members. For the time being those methods can be moved onto CuckooSearchOptimizer and be private, then if we need to use them from other places they can be easily moved and made more accessible.
| private final double delta; | ||
| private final int populationSize; | ||
| private int [][] setOfSolutions; | ||
| private final int maxIteration; |
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All the final variables here should not be final and need to be tagged @Config (with appropriate description fields) so they are automatically captured by the provenance system.
| * @param totalNumberOfFeatures The number of features in the given dataset | ||
| * @return The population of subsets of selected features | ||
| */ | ||
| private int[][] GeneratePopulation(int totalNumberOfFeatures) { |
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As mentioned elsewhere this method should accept a SplittableRandom rather than make a fresh RNG each time.
| /** | ||
| * This record is used to hold subset of features with its corresponding fitness score | ||
| */ | ||
| record FeatureSet_FScore_Container(int[] subSet, double score) { } |
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I used this record in order to store the solutions with their corresponding fitness values in order to use this to arrange the solutions based on their fitness scores using Comparator
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Ah, sorry, I was suggesting this record should be renamed CuckooSearchFeatureSet as it's specific to this approach and it's currently got a very general name.
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Ah, I am sorry, I don't get your note as it should be. You are right I should change the name, and I am currently working on the FJP issue. Thanks Dr. Adam for your suggestions. All of your suggestions are suitable, and I know that I still need more time to learn more and more in programming in Java (my best language)
| KNNClassifierOptions classifier = new KNNClassifierOptions(); | ||
| CrossValidation<Label, LabelEvaluation> crossValidation = new CrossValidation<>(classifier.getTrainer(), selectedFeatureDataset, new LabelEvaluator(), 10); | ||
| double avgAccuracy = 0d; | ||
| for (Pair<LabelEvaluation, Model<Label>> ACC : crossValidation.evaluate()) |
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We always use curly braces even for single line for loops and if statements.
| import FS_Wrapper_Approaches.Discreeting.Binarizing; | ||
| import FS_Wrapper_Approaches.Discreeting.TransferFunction; | ||
| import com.oracle.labs.mlrg.olcut.util.Pair; | ||
| import org.tribuo.*; |
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All imports should be explicit, no star imports for packages.
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Thank you for your suggestions, all of them are great and I will do them, but currently I am working on my PhD proposal and I have meetings with my advisor during the current month, so I will make these suggestions during the next one. |
…ification/fs/wrapper directory
Description
< Adding a wrapper FS approach based on binary Cuckoo Search algorithm to Tribuo engine>
Motivation
< Recently Evolutionary Computation have shown alot of good results in a wide range of technologies especially in engineering problems. Moreover, many published research papers include the utilizing of EO algorithms for FS and inform good results. Therefore, adding a wrapper FS model to Tribuo engine would be great >
< Please link any relevant issues or PRs >
Paper reference
< Rodrigues, D. et al. BCS: A Binary Cuckoo Search algorithm for feature selection. Proc. - IEEE Int. Symp. Circuits Syst. 465–468 (2013) doi:10.1109/ISCAS.2013.6571881. >