DataManyo

Relief 알고리즘을 직접 코드로 구현하는 것은 조금 복잡할 수 있지만, 이해를 돕기 위해 기본적인 아이디어를 파이썬 코드로 표현해 보겠습니다.

다음 코드는 실제 Relief 알고리즘의 간략화 된 버전이며, 실제로 작동하려면 입력 데이터와 함께 조정 및 추가 구현이 필요합니다.

또한, 이 코드는 scikit-learn 라이브러리의 K-Nearest Neighbors(KNN) 알고리즘을 사용하여 가장 가까운 이웃을 찾습니다.

import numpy as np
from sklearn.neighbors import NearestNeighbors

def relief(X, y, m):
    """
    X : Data instances
    y : Class labels
    m : Number of sampled instances
    """

    # Initialize a weight vector to zeros
    weights = np.zeros(X.shape[1])
    for i in range(m):
        # Randomly select an instance
        idx = np.random.randint(0, X.shape[0])

        # Split the data into same and different class
        same_class = X[y==y[idx]]
        diff_class = X[y!=y[idx]]

        # Find the nearest neighbor from the same class
        nn_same = NearestNeighbors(n_neighbors=1).fit(same_class)
        distances_same, indices_same = nn_same.kneighbors(X[idx].reshape(1, -1))
        near_same = same_class[indices_same[0][0]]

        # Find the nearest neighbor from the different class
        nn_diff = NearestNeighbors(n_neighbors=1).fit(diff_class)
        distances_diff, indices_diff = nn_diff.kneighbors(X[idx].reshape(1, -1))
        near_diff = diff_class[indices_diff[0][0]]

        # Update the weights
        weights -= np.square(X[idx] - near_same)
        weights += np.square(X[idx] - near_diff)

    return weights

위의 코드는 Relief 알고리즘의 간략화 된 버전이기 때문에, 실제 데이터세트에 적용하려면 추가적인 사전 처리 단계와 최적화가 필요할 수 있습니다. 예를 들어, 코드는 현재 수치형 속성에만 적용됩니다. 범주형 속성이 있는 경우, 해당 속성을 적절하게 처리해야 할 것입니다. 또한, 거리 메트릭에 따라 결과가 크게 달라질 수 있으므로, 문제에 가장 적합한 메트릭을 선택하는 것이 중요합니다.