"""
EnhancedFeatureExtraction
Generated by Eden via recursive self-improvement
2025-11-01 07:41:28.061771
"""

import numpy as np
from sklearn.feature_selection import SelectKBest, f_classif

# Function to extract top k features based on their statistical significance in classification tasks
def enhanced_feature_extraction(data, labels, k):
    """
    Extracts the top k most significant features from a dataset using ANOVA F-value.

    Parameters:
        data (numpy.ndarray): The input feature matrix of shape (n_samples, n_features).
        labels (numpy.ndarray): The target vector of shape (n_samples, ).
        k (int): The number of top features to select.

    Returns:
        numpy.ndarray: A transformed dataset containing only the selected features.
    """
    # Using SelectKBest from sklearn for feature selection
    selector = SelectKBest(score_func=f_classif, k=k)
    data_transformed = selector.fit_transform(data, labels)

    return data_transformed

# Example usage
def main():
    # Simulate a dataset with 100 samples and 50 features
    np.random.seed(0)
    X = np.random.rand(100, 50)  # Feature matrix
    y = np.random.randint(2, size=100)  # Target vector

    k_features = 10  # Number of top features to select
    transformed_data = enhanced_feature_extraction(X, y, k=k_features)

    print(f"Original data shape: {X.shape}")
    print(f"Transformed data shape after feature extraction: {transformed_data.shape}")

if __name__ == "__main__":
    main()