class MiniBatchKMeansImpl(): def __init__(self, n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01): self._hyperparams = { 'n_clusters': n_clusters, 'init': init, 'max_iter': max_iter, 'batch_size': batch_size, 'verbose': verbose, 'compute_labels': compute_labels, 'random_state': random_state, 'tol': tol, 'max_no_improvement': max_no_improvement, 'init_size': init_size, 'n_init': n_init, 'reassignment_ratio': reassignment_ratio} self._wrapped_model = SKLModel(**self._hyperparams) def fit(self, X, y=None): if (y is not None): self._wrapped_model.fit(X, y) else: self._wrapped_model.fit(X) return self def transform(self, X): return self._wrapped_model.transform(X) def predict(self, X): return self._wrapped_model.predict(X)