also save shuffled keys, jsut in case

package/chemicalchecker/util/splitter/neighbortriplet.py

@@ -377,6 +377,8 @@ class NeighborTripletTraintest(object):
         combo_dists = dict()
         with h5py.File(out_file, "w") as fh:
             fh.create_dataset('x', data=X)
+            fh.create_dataset('x_ink', data=np.array(
+                X_inks, dtype=DataSignature.string_dtype()))
             if mean_center_x:
                 fh.create_dataset(
                     'scaler',
@@ -441,7 +443,8 @@ class NeighborTripletTraintest(object):
 
                 # save list of split indeces
 
-                #anchors_split = np.repeat(np.arange(len(neig_idxs)), triplet_per_mol)
+                # anchors_split = np.repeat(np.arange(len(neig_idxs)),
+                # triplet_per_mol)
                 easy_a_split = list()
                 easy_p_split = list()
                 easy_n_split = list()
@@ -505,8 +508,8 @@ class NeighborTripletTraintest(object):
                     hard_n_split.extend(h_negatives)
 
                     # easy negatives (sampled from everywhere; in general should be fine altough it may sample positives...)
-                    #e_negatives = np.random.choice(len(neig_idxs), triplet_per_mol*2, replace=True)
-                    #e_negatives = np.random.choice(list(e_negatives.difference(neig_idxs[idx])), triplet_per_mol, replace=True)
+                    # e_negatives = np.random.choice(len(neig_idxs), triplet_per_mol*2, replace=True)
+                    # e_negatives = np.random.choice(list(e_negatives.difference(neig_idxs[idx])), triplet_per_mol, replace=True)
                     # if too slow just uncomment the following and comment the
                     # above
                     e_negatives = np.random.choice(
@@ -515,7 +518,8 @@ class NeighborTripletTraintest(object):
 
                 # get reference ids
                 NeighborTripletTraintest.__log.info("Mapping triplets")
-                #anchors_ref = [split_ref_map[split2][x] for x in anchors_split]
+                # anchors_ref = [split_ref_map[split2][x] for x in
+                # anchors_split]
                 easy_a_ref = [split_ref_map[split2][x] for x in easy_a_split]
                 easy_p_ref = [split_ref_map[split1][x] for x in easy_p_split]
                 easy_n_ref = [split_ref_map[split1][x] for x in easy_n_split]
@@ -704,7 +708,8 @@ class NeighborTripletTraintest(object):
                 if sharedx_trim is not None:
                     X = sharedx_trim
                 else:
-                    X = sharedx[:, np.argwhere(np.repeat(trim_mask, 128)).ravel()]
+                    X = sharedx[:, np.argwhere(
+                        np.repeat(trim_mask, 128)).ravel()]
         else:
             NeighborTripletTraintest.__log.debug('Reading X in memory')
             if trim_mask is None:
@@ -713,7 +718,8 @@ class NeighborTripletTraintest(object):
                 if sharedx_trim is not None:
                     X = sharedx_trim
                 else:
-                    X = reader.get_x_columns(np.argwhere(np.repeat(trim_mask, 128)).ravel())
+                    X = reader.get_x_columns(np.argwhere(
+                        np.repeat(trim_mask, 128)).ravel())
         NeighborTripletTraintest.__log.debug('X shape: %s' % str(X.shape))
         # default mask is not masking
         if mask_fn is None: