Dataset sorts examples ?

config_files/config_test.yml

 # The base configuration of the benchmark
-log: True
+log: False
 name: ["plausible",]
 label: "_"
 file_type: ".hdf5"
@@ -19,15 +19,15 @@ track_tracebacks: False
 multiclass_method: "oneVersusOne"
 split: 0.49
 nb_folds: 2
-nb_class: 3
+nb_class: 2
 classes:
-type: ["multiview",]
+type: ["multiview","monoview"]
 algos_monoview: ["decision_tree" ]
-algos_multiview: ["svm_jumbo_fusion",]
+algos_multiview: ["weighted_linear_early_fusion",]
 stats_iter: 2
 metrics: ["accuracy_score", "f1_score"]
-metric_princ: "f1_score"
-hps_type: "randomized_search"
+metric_princ: "accuracy_score"
+hps_type: "None"
 hps_iter: 1
 
 

multiview_platform/mono_multi_view_classifiers/utils/dataset.py

@@ -195,9 +195,9 @@ class RAMDataset(Dataset):
         if type(example_indices) is int:
             return self.views[view_index][example_indices, :]
         else:
-            example_indices = np.array(example_indices)
-            sorted_indices = np.argsort(example_indices)
-            example_indices = example_indices[sorted_indices]
+            example_indices = np.asarray(example_indices)
+            # sorted_indices = np.argsort(example_indices)
+            # example_indices = example_indices[sorted_indices]
             if not self.are_sparse[view_index]:
                 return self.views[view_index][
                        example_indices, :]
@@ -452,12 +452,11 @@ class HDF5Dataset(Dataset):
             return self.dataset["View" + str(view_index)][example_indices, :]
         else:
             example_indices = np.array(example_indices)
-            sorted_indices = np.argsort(example_indices)
-            example_indices = example_indices[sorted_indices]
+            # sorted_indices = np.argsort(example_indices)
+            # example_indices = example_indices[sorted_indices]
 
             if not self.dataset["View" + str(view_index)].attrs["sparse"]:
-                return self.dataset["View" + str(view_index)][()][example_indices, :][
-                       np.argsort(sorted_indices), :]
+                return self.dataset["View" + str(view_index)][()][example_indices, :]#[np.argsort(sorted_indices), :]
             else:
                 sparse_mat = sparse.csr_matrix(
                     (self.dataset["View" + str(view_index)]["data"][()],