import numpy as np
np.random.seed(42)

class sample(object):
    def __init__(self, X, n_subspace):
        self.idx_subspace = self.random_subspace(X, n_subspace)

    def __call__(self, X, y):
        idx_obj = self.bootstrap_sample(X)
        X_sampled, y_sampled = self.get_subsample(X, y, self.idx_subspace, idx_obj)
        return X_sampled, y_sampled

    @staticmethod
    def bootstrap_sample(X, random_state=42):
        return np.unique(np.random.choice(X.shape[0], X.shape[0]))

    @staticmethod
    def random_subspace(X, n_subspace, random_state=42):
        return np.random.choice(X.shape[1], n_subspace, replace=False)

    @staticmethod
    def get_subsample(X, y, idx_subspace, idx_obj):
        X_sampled = X[idx_obj, :][:, idx_subspace]
        y_sampled = y[idx_obj]
        return X_sampled, y_sampled