[MNT] remove redundant codes

learnware/specification/regular/table/rkme.py

@@ -15,25 +15,11 @@ from typing import Tuple, Any, List, Union, Dict
 import scipy
 from sklearn.cluster import MiniBatchKMeans
 
-# try:
-#     import faiss
-
-#     ver = faiss.__version__
-#     _FAISS_INSTALLED = ver >= "1.7.1"
-# except ImportError:
-#     _FAISS_INSTALLED = False
-
 from ..base import RegularStatsSpecification
 from ....logger import get_module_logger
 
 logger = get_module_logger("rkme")
 
-# if not _FAISS_INSTALLED:
-#     logger.warning(
-#         "Required faiss version >= 1.7.1 is not detected! Please run 'conda install -c pytorch faiss-cpu' first"
-#     )
-
-
 class RKMETableSpecification(RegularStatsSpecification):
     """Reduced Kernel Mean Embedding (RKME) Specification"""
 
@@ -128,7 +114,7 @@ class RKMETableSpecification(RegularStatsSpecification):
             self.beta = torch.from_numpy(self.beta).double().to(self.device)
             return
 
-        # Initialize Z by clustering, utiliing kmeans or faiss to speed up the process.
+        # Initialize Z by clustering, utiliing kmeans to speed up the process.
         self._init_z_by_kmeans(X, K)
         self._update_beta(X, nonnegative_beta)
 
@@ -140,23 +126,6 @@ class RKMETableSpecification(RegularStatsSpecification):
         # Reshape to original dimensions
         self.z = self.z.reshape(Z_shape)
 
-    # def _init_z_by_faiss(self, X: Union[np.ndarray, torch.tensor], K: int):
-    #     """Intialize Z by faiss clustering.
-
-    #     Parameters
-    #     ----------
-    #     X : np.ndarray or torch.tensor
-    #         Raw data in np.ndarray format or torch.tensor format.
-    #     K : int
-    #         Size of the construced reduced set.
-    #     """
-    #     X = X.astype("float32")
-    #     numDim = X.shape[1]
-    #     kmeans = faiss.Kmeans(numDim, K, niter=100, verbose=False)
-    #     kmeans.train(X)
-    #     center = torch.from_numpy(kmeans.centroids).double()
-    #     self.z = center
-
     def _init_z_by_kmeans(self, X: Union[np.ndarray, torch.tensor], K: int):
         """Intialize Z by kmeans clustering.
 
@@ -590,25 +559,8 @@ def rkme_solve_qp(K: np.ndarray, C: np.ndarray):
     A = np.array(np.ones((1, n)))
     A = scipy.sparse.csc_matrix(A)
     b = np.array(np.ones((1, 1)))
-    # sol = solve_qp(P, q, G, h, A, b, solver="clarabel") # Requires the sum of x to be 1
-    # sol = solver_qp(P, q, G, h, solver="clarabel") # Otherwise
     problem = Problem(P, q, G, h, A, b)
     solution = solve_problem(problem, solver="clarabel")
     w = solution.x
     w = torch.from_numpy(w).reshape(-1)
     return w, solution.obj
-
-    # from cvxopt import solvers, matrix
-    # n = K.shape[0]
-    # P = matrix(K)
-    # q = matrix(-C)
-    # G = matrix(-np.eye(n))
-    # h = matrix(np.zeros((n, 1)))
-    # A = matrix(np.ones((1, n)))
-    # b = matrix(np.ones((1, 1)))
-    # solvers.options["show_progress"] = False
-    # sol = solvers.qp(P, q, G, h, A, b)  # Requires the sum of x to be 1
-    # # sol = solvers.qp(P, q, G, h) # Otherwise
-    # w = np.array(sol["x"])
-    # w = torch.from_numpy(w).reshape(-1)
-    # return w, sol["primal objective"]