In K-fold cross-validation, the aim is to generate K training/validation set pair, where training and validation sets on fold i do no overlap. First, we divide the dataset X into K parts as X₁; X₂; ... ; X_K. Then for each fold i, we use X_i as the validation set and the remaining as the training set.

Possible values of K are 10 or 30. One extreme case of K-fold cross-validation is leave-one-out, where K = N and each validation set has only one instance. If we have more computation power, we can have multiple runs of K-fold cross-validation, such as 10 x 10 cross-validation or 5 x 2 cross-validation.

If we have very small datasets, we do not insist on the non-overlap of training and validation sets. In bootstrapping, we generate K multiple training sets, where each training set contains N examples (like the original dataset). To get N examples, we draw examples with replacement. For the validation set, we use the original dataset. The drawback of bootstrapping is that the bootstrap samples overlap more than the cross-validation sample, hence they are more dependent.

You can also see Java, Python, Cython, C++, C, Swift, Php, or C# repository.

• Node.js 14 or higher
• Git

To check if you have a compatible version of Node.js installed, use the following command:

node -v

You can find the latest version of Node.js here.

Install the latest version of Git.

npm install nlptoolkit-sampling

In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:

git clone <your-fork-git-link>

A directory called util will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/sampling-js.git

Steps for opening the cloned project:

• Start IDE
• Select File | Open from main menu
• Choose Sampling-Js file
• Select open as project option
• Couple of seconds, dependencies will be downloaded.

• CrossValidation
• Bootstrap
• KFoldCrossValidation
• StratifiedKFoldCrossValidation

k. eğitim kümesini elde etmek için

getTrainFold(k: number): Array<T>

k. test kümesini elde etmek için

getTestFold(k: number): Array<T>

Bootstrap için BootStrap sınıfı

Bootstrap(instanceList: Array<T>, seed: number)

Örneğin elimizdeki veriler a adlı ArrayList'te olsun. Bu veriler üstünden bir bootstrap örneklemi tanımlamak için (5 burada rasgelelik getiren seed'i göstermektedir. 5 değiştirilerek farklı samplelar elde edilebilir)

bootstrap = Bootstrap(a, 5)

ardından üretilen sample'ı çekmek için ise

sample = bootstrap.getSample()

yazılır.

K kat çapraz geçerleme için KFoldCrossValidation sınıfı

KFoldCrossValidation(instanceList: Array<T>, K: number, seed: number)

Örneğin elimizdeki veriler a adlı ArrayList'te olsun. Bu veriler üstünden 10 kat çapraz geçerleme yapmak için (2 burada rasgelelik getiren seed'i göstermektedir. 2 değiştirilerek farklı samplelar elde edilebilir)

kfold = KFoldCrossValidation(a, 10, 2)

ardından yukarıda belirtilen getTrainFold ve getTestFold metodları ile sırasıyla i. eğitim ve test kümeleri elde edilebilir.

Stratified K kat çapraz geçerleme için StratifiedKFoldCrossValidation sınıfı

StratifiedKFoldCrossValidation(instanceLists: Array<Array<T>>, K: number, seed: number)

Örneğin elimizdeki veriler a adlı ArrayList of listte olsun. Stratified bir çapraz geçerlemede sınıflara ait veriler o sınıfın oranında temsil edildikleri için her bir sınıfa ait verilerin ayrı ayrı ArrayList'te olmaları gerekmektedir. Bu veriler üstünden 30 kat çapraz geçerleme yapmak için (4 burada rasgelelik getiren seed'i göstermektedir. 4 değiştirilerek farklı samplelar elde edilebilir)

stratified = StratifiedKFoldCrossValidation(a, 30, 4)

ardından yukarıda belirtilen getTrainFold ve getTestFold metodları ile sırasıyla i. eğitim ve test kümeleri elde edilebilir.

1. main and types are important when this package will be imported.

  "main": "dist/index.js",
  "types": "dist/index.d.ts",

2. Dependencies should be maximum (not only direct but also indirect references should also be given), everything directly in the code should be given here.

  "dependencies": {
    "nlptoolkit-corpus": "^1.0.12",
    "nlptoolkit-dictionary": "^1.0.14",
    "nlptoolkit-morphologicalanalysis": "^1.0.19",
    "nlptoolkit-xmlparser": "^1.0.7"
  }

1. Compiler flags currently includes nodeNext for importing.

  "compilerOptions": {
    "outDir": "dist",
    "module": "nodeNext",
    "sourceMap": true,
    "noImplicitAny": true,
    "removeComments": false,
    "declaration": true,
  },

2. tests, node_modules and dist should be excluded.

  "exclude": [
    "tests",
    "node_modules",
    "dist"
  ]

1. Should include all ts classes.

export * from "./CategoryType"
export * from "./InterlingualDependencyType"
export * from "./InterlingualRelation"
export * from "./Literal"

1. Add data files to the project folder. Subprojects should include all data files of the parent projects.

1. Classes should be defined as exported.

export class JCN extends ICSimilarity{

2. Do not forget to comment each function.

    /**
     * Computes JCN wordnet similarity metric between two synsets.
     * @param synSet1 First synset
     * @param synSet2 Second synset
     * @return JCN wordnet similarity metric between two synsets
     */
    computeSimilarity(synSet1: SynSet, synSet2: SynSet): number {

3. Function names should follow caml case.

    setSynSetId(synSetId: string){

4. Write getter and setter methods.

    getRelation(index: number): Relation{
    setName(name: string){

5. Use standard javascript test style.

describe('SimilarityPathTest', function() {
    describe('SimilarityPathTest', function() {
        it('testComputeSimilarity', function() {
            let turkish = new WordNet();
            let similarityPath = new SimilarityPath(turkish);
            assert.strictEqual(32.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0656390"), turkish.getSynSetWithId("TUR10-0600460")));
            assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0412120"), turkish.getSynSetWithId("TUR10-0755370")));
            assert.strictEqual(13.0, similarityPath.computeSimilarity(turkish.getSynSetWithId("TUR10-0195110"), turkish.getSynSetWithId("TUR10-0822980")));
        });
    });
});

6. Enumerated types should be declared with enum.

export enum CategoryType {
    MATHEMATICS, SPORT, MUSIC, SLANG, BOTANIC,
    PLURAL, MARINE, HISTORY, THEOLOGY, ZOOLOGY,
    METAPHOR, PSYCHOLOGY, ASTRONOMY, GEOGRAPHY, GRAMMAR,
    MILITARY, PHYSICS, PHILOSOPHY, MEDICAL, THEATER,
    ECONOMY, LAW, ANATOMY, GEOMETRY, BUSINESS,
    PEDAGOGY, TECHNOLOGY, LOGIC, LITERATURE, CINEMA,
    TELEVISION, ARCHITECTURE, TECHNICAL, SOCIOLOGY, BIOLOGY,
    CHEMISTRY, GEOLOGY, INFORMATICS, PHYSIOLOGY, METEOROLOGY,
    MINERALOGY
}

7. If there are multiple constructors for a class, define them as constructor1, constructor2, ..., then from the original constructor call these methods.

    constructor1(symbol: any){
    constructor2(symbol: any, multipleFile: MultipleFile) {
    constructor(symbol: any, multipleFile: MultipleFile = undefined) {
        if (multipleFile == undefined){
            this.constructor1(symbol);
        } else {
            this.constructor2(symbol, multipleFile);
        }
    }

8. Importing should be done via import method with referencing the node-modules.

import {Corpus} from "nlptoolkit-corpus/dist/Corpus";
import {Sentence} from "nlptoolkit-corpus/dist/Sentence";

9. Use xmlparser package for parsing xml files.

	var doc = new XmlDocument("test.xml")
	doc.parse()
	let root = doc.getFirstChild()
	let firstChild = root.getFirstChild()