[FIX] fix pfs

examples/dataset_pfs_workflow/main.py

@@ -55,7 +55,7 @@ class PFSDatasetWorkflow:
         pfs = Dataloader()
         pfs.regenerate_data()
 
-        algo_list = ["ridge"] # , "lgb"
+        algo_list = ["ridge"] # "ridge", "lgb"
         for algo in algo_list:
             pfs.set_algo(algo)
             pfs.retrain_models()
@@ -76,7 +76,8 @@ class PFSDatasetWorkflow:
             semantic_spec = semantic_specs[0]
             semantic_spec["Name"]["Values"] = "learnware_%d" % (idx)
             semantic_spec["Description"]["Values"] = "test_learnware_number_%d" % (idx)
-            easy_market.add_learnware(zip_path, semantic_spec)
+            x = easy_market.add_learnware(zip_path, semantic_spec)
+            print(x)
 
         print("Total Item:", len(easy_market))
         # curr_inds = easy_market._get_ids()
@@ -132,7 +133,7 @@ class PFSDatasetWorkflow:
                 rmtree(dir_path)
 
     def test(self, regenerate_flag=False):
-        self.prepare_learnware(regenerate_flag)
+        # self.prepare_learnware(regenerate_flag)
         self._init_learnware_market()
 
         easy_market = instantiate_learnware_market(market_id="pfs", name="easy")
@@ -170,7 +171,7 @@ class PFSDatasetWorkflow:
                 pred_y = learnware.predict(test_x)
                 loss_list.append(pfs.score(test_y, pred_y))
             print(
-                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}"
+                f"Top1-score: {sorted_score_list[0]}, learnware_id: {single_learnware_list[0].id}, loss: {loss_list[0]}, random: {np.mean(loss_list)}"
             )
 
             mixture_id = " ".join([learnware.id for learnware in mixture_learnware_list])

learnware/specification/regular/table/rkme.py

@@ -15,13 +15,13 @@ from typing import Tuple, Any, List, Union, Dict
 import scipy
 from sklearn.cluster import MiniBatchKMeans
 
-try:
-    import faiss
+# try:
+#     import faiss
 
-    ver = faiss.__version__
-    _FAISS_INSTALLED = ver >= "1.7.1"
-except ImportError:
-    _FAISS_INSTALLED = False
+#     ver = faiss.__version__
+#     _FAISS_INSTALLED = ver >= "1.7.1"
+# except ImportError:
+#     _FAISS_INSTALLED = False
 
 from ..base import RegularStatsSpecification
 from ....logger import get_module_logger
@@ -129,7 +129,7 @@ class RKMETableSpecification(RegularStatsSpecification):
             return
 
         # Initialize Z by clustering, utiliing kmeans or faiss to speed up the process.
-        self._init_z_by_faiss(X, K)
+        self._init_z_by_kmeans(X, K)
         self._update_beta(X, nonnegative_beta)
 
         # Alternating optimize Z and beta
@@ -140,22 +140,22 @@ 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_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.
@@ -168,7 +168,7 @@ class RKMETableSpecification(RegularStatsSpecification):
             Size of the construced reduced set.
         """
         X = X.astype("float32")
-        kmeans = MiniBatchKMeans(n_clusters=K, max_iter=100, verbose=False, n_init="auto")
+        kmeans = MiniBatchKMeans(n_clusters=K, max_iter=100, verbose=False, n_init='auto')
         kmeans.fit(X)
         center = torch.from_numpy(kmeans.cluster_centers_).double()
         self.z = center
@@ -578,6 +578,7 @@ def rkme_solve_qp(K: np.ndarray, C: np.ndarray):
         K = K.cpu().numpy()
     if torch.is_tensor(C):
         C = C.cpu().numpy()
+
     n = K.shape[0]
     P = np.array(K)
     P = scipy.sparse.csc_matrix(P)
@@ -588,11 +589,25 @@ 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)
-    sol = solve_problem(problem, solver="clarabel")
-    w = sol.x
+    solution = solve_problem(problem, solver="clarabel")
+    w = solution.x
     w = torch.from_numpy(w).reshape(-1)
-    return w, sol.obj
+    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"]