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 Python, Java, C++, C, Swift, Js, Php, or C# repository.

• Python 3.7 or higher
• Git

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

python -V

You can find the latest version of Python here.

Install the latest version of Git.

pip3 install NlpToolkit-Sampling-Cy

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 Sampling will be created. Or you can use below link for exploring the code:

git clone https://github.com/starlangsoftware/Sampling-Cy.git

Steps for opening the cloned project:

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

• CrossValidation
• Bootstrap
• KFoldCrossValidation
• StratifiedKFoldCrossValidation

k. eğitim kümesini elde etmek için

getTrainFold(self, k: int) -> list

k. test kümesini elde etmek için

getTestFold(self, k: int) -> list

Bootstrap için BootStrap sınıfı

Bootstrap(self, instanceList: list, seed: int)

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(self, instanceList: list, K: int, seed: int)

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(self, instanceLists: list, K: int, seed: int)

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. Do not forget to set package list. All subfolders should be added to the package list.

    packages=['Classification', 'Classification.Model', 'Classification.Model.DecisionTree',
              'Classification.Model.Ensemble', 'Classification.Model.NeuralNetwork',
              'Classification.Model.NonParametric', 'Classification.Model.Parametric',
              'Classification.Filter', 'Classification.DataSet', 'Classification.Instance', 'Classification.Attribute',
              'Classification.Parameter', 'Classification.Experiment',
              'Classification.Performance', 'Classification.InstanceList', 'Classification.DistanceMetric',
              'Classification.StatisticalTest', 'Classification.FeatureSelection'],

2. Package name should be lowercase and only may include _ character.

    name='nlptoolkit_math',

1. Do not forget to comment each function.

    def __broadcast_shape(self, shape1: Tuple[int, ...], shape2: Tuple[int, ...]) -> Tuple[int, ...]:
        """
        Determines the broadcasted shape of two tensors.

        :param shape1: Tuple representing the first tensor shape.
        :param shape2: Tuple representing the second tensor shape.
        :return: Tuple representing the broadcasted shape.
        """

2. Function names should follow caml case.

    def addItem(self, item: str):

3. Local variables should follow snake case.

	det = 1.0
	copy_of_matrix = copy.deepcopy(self)

4. Class variables should be declared in each file.

class Eigenvector(Vector):
    eigenvalue: float

5. Variable types should be defined for function parameters and class variables.

    def getIndex(self, item: str) -> int:

6. For abstract methods, use ABC package and declare them with @abstractmethod.

    @abstractmethod
    def train(self, train_set: list[Tensor]):
        pass

7. For private methods, use __ as prefix in their names.

    def __infer_shape(self, data: Union[List, List[List], List[List[List]]]) -> Tuple[int, ...]:

8. For private class variables, use __ as prefix in their names.

class Matrix(object):
    __row: int
    __col: int
    __values: list[list[float]]

9. Write __repr__ class methods as toString methods
10. Write getter and setter class methods.

    def getOptimizer(self) -> Optimizer:
        return self.optimizer
    def setValue(self, value: Optional[Tensor]) -> None:
        self._value = value

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

    def constructor1(self):
        self.__values = []
        self.__size = 0

    def constructor2(self, values: list):
        self.__values = values.copy()
        self.__size = len(values)

    def __init__(self,
                 valuesOrSize=None,
                 initial=None):
        if valuesOrSize is None:
            self.constructor1()
        elif isinstance(valuesOrSize, list):
            self.constructor2(valuesOrSize)

12. Extend test classes from unittest and use separate unit test methods.

class TensorTest(unittest.TestCase):

    def test_inferred_shape(self):
        a = Tensor([[1.0, 2.0], [3.0, 4.0]])
        self.assertEqual((2, 2), a.getShape())

    def test_shape(self):
        a = Tensor([1.0, 2.0, 3.0])
        self.assertEqual((3, ), a.getShape())

13. Enumerated types should be used when necessary as enum classes.

class AttributeType(Enum):
    """
    Continuous Attribute
    """
    CONTINUOUS = auto()
    """
    Discrete Attribute
    """
    DISCRETE = auto()