Adjust splitting

proteinflow/__init__.py

@@ -339,8 +339,8 @@ def generate_data(
         the sequence similarity threshold for excluding chains
     exclude_clusters : bool, default False
         if `True`, exclude clusters that contain chains similar to chains in the `exclude_chains` list
-    exclude_based_on_cdr : {"H1", "H2", "H3", "L1", "L2", "L3"}, optional
-        if given and `exclude_clusters` is `True` + the dataset is SAbDab, exclude files based on only the given CDR clusters
+    exclude_based_on_cdr : list, optional
+        if given and `exclude_clusters` is `True` + the dataset is SAbDab, exclude files based on only the given CDR clusters (choose from "H1", "H2", "H3", "L1", "L2", "L3")
     load_ligands : bool, default False
         if `True`, load ligands from the PDB files
     exclude_chains_without_ligands : bool, default False
@@ -506,8 +506,8 @@ def split_data(
         the sequence similarity threshold for excluding chains
     exclude_clusters : bool, default False
         if `True`, exclude clusters that contain chains similar to chains in the `exclude_chains` list
-    exclude_based_on_cdr : {"H1", "H2", "H3", "L1", "L2", "L3"}, optional
-        if given and `exclude_clusters` is `True` + the dataset is SAbDab, exclude files based on only the given CDR clusters
+    exclude_based_on_cdr : list, optional
+        if given and `exclude_clusters` is `True` + the dataset is SAbDab, exclude files based on only the given CDR clusters (choose from "H1", "H2", "H3", "L1", "L2", "L3")
     random_seed : int, default 42
         random seed for reproducibility (set to `None` to use a random seed)
     exclude_chains_without_ligands : bool, default False

proteinflow/cli.py

@@ -193,6 +193,7 @@ def download(**kwargs):
 @click.option(
     "--exclude_based_on_cdr",
     type=click.Choice(["L1", "L2", "L3", "H1", "H2", "H3"]),
+    multiple=True,
     help="if given and exclude_clusters is true + the dataset is SAbDab, exclude files based on only the given CDR clusters",
 )
 @click.option(
@@ -302,6 +303,7 @@ def generate(**kwargs):
 @click.option(
     "--exclude_based_on_cdr",
     type=click.Choice(["L1", "L2", "L3", "H1", "H2", "H3"]),
+    multiple=True,
     help="if given and exclude_clusters is true + the dataset is SAbDab, exclude files based on only the given CDR clusters",
 )
 @click.option(

proteinflow/split/__init__.py

@@ -1395,11 +1395,7 @@ def _exclude_biounits(
 
     Since `proteinflow` assumes splitting at the level of biounits, when using `exclude_clusters` the dictionaries are adjusted to exclude
     full biounits that the newly excluded chains / CDRs are part of. This is done by moving the full biounits to the excluded set
-    and removing the rest of the clusters they belong to from training / test / validation.
-
-    For example, if for antibody Ab CDR H1 is in cluster A, CDR H2 is in cluster B and CDR H3 is in cluster C, and cluster C is in
-    the excluded set, then clusters A and B are removed from the training / test / validation sets and added to the excluded set with only the Ab CDRs.
-    The files for the other biounits that are part of the excluded clusters are also moved to the excluded set but not added to the split dictionary.
+    and removing the corresponding entries from all training / test / validation clusters.
 
     """
     set_to_exclude = set(excluded_biounits)
@@ -1416,8 +1412,9 @@ def _exclude_biounits(
             for i, chain in enumerate(clusters_dict[cluster]):
                 if chain[0] in set_to_exclude:
                     if exclude_clusters:
-                        if exclude_based_on_cdr is not None and cluster.endswith(
-                            exclude_based_on_cdr
+                        if (
+                            exclude_based_on_cdr is not None
+                            and cluster.split("__")[-1] in exclude_based_on_cdr
                         ):
                             exclude_whole_cluster = True
                         elif exclude_based_on_cdr is None:
@@ -1444,19 +1441,15 @@ def _exclude_biounits(
         test_clusters_dict,
     ]:
         for cluster in list(clusters_dict.keys()):
-            excluded_biounit_in_cluster = False
-            if cluster in excluded_clusters_dict:
-                excluded_biounit_in_cluster = True
+            to_exclude = []
             for i, (file, chain) in enumerate(clusters_dict[cluster]):
                 if file in excluded_biounits:
-                    excluded_biounit_in_cluster = True
-                    excluded_clusters_dict[cluster].append((file, chain))
-                    excluded_biounits.add(file)
-            # remove cluster from training / validation / test set if at least one biounit in the cluster is excluded
-            if exclude_clusters and excluded_biounit_in_cluster:
-                chains = clusters_dict.pop(cluster)
-                excluded_biounits.update([x[0] for x in chains])
-    excluded_clusters_dict = {k: list(v) for k, v in excluded_clusters_dict.items()}
+                    to_exclude.append(i)
+            clusters_dict[cluster] = [
+                x for i, x in enumerate(clusters_dict[cluster]) if i not in to_exclude
+            ]
+            if len(clusters_dict[cluster]) == 0:
+                clusters_dict.pop(cluster)
     return (
         train_clusters_dict,
         valid_clusters_dict,
@@ -1482,7 +1475,7 @@ def _split_data(
         A list of files to exclude from the dataset
     exclude_clusters : bool, default False
         If True, exclude all files in a cluster if at least one file in the cluster is in `excluded_files`
-    exclude_based_on_cdr : str, optional
+    exclude_based_on_cdr : list, optional
         If not `None`, exclude all files in a cluster if the cluster name does not end with `exclude_based_on_cdr`
 
     """