Add bylibrarydedup

oo-method-test-split.py

@@ -37,6 +37,11 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
             name="bylibrary",
             version=datasets.Version("1.0.0"),
             description="Split so that library functions (those appearing in >1 exe) are used for training, and non-library functions are used for testing",
+        ),
+        datasets.BuilderConfig(
+            name="bylibrarydedup",
+            version=datasets.Version("1.0.0"),
+            description="Split so that library functions (those appearing in >1 exe) are used for training, and non-library functions are used for testing. Only one example per function name is retained.",
         )
 
     ]
@@ -118,7 +123,7 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
 
             ]
         
-        elif self.config.name == "bylibrary":
+        elif self.config.name in ["bylibrary", "bylibrarydedup"]:
             # A function (name) is a library function if it appears in more than one Exename
 
             # this is (('func', 'oo.exe'): 123)
@@ -133,13 +138,22 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
             grouped = {k: [b for _,b in g] for k, g in grouped}
 
             library_func_names = {f for f, exes in grouped.items() if len(exes) > 1}
+            library_func_names_dedup = {(f, exes[0]) for f, exes in grouped.items() if len(exes) > 1}
             nonlibrary_func_names = {f for f, exes in grouped.items() if len(exes) == 1}
 
+            train_filter_fun = None
+            if self.config.name == "bylibrary":
+                train_filter_fun = lambda r: r['Name'] in library_func_names
+            elif self.config.name == "bylibrarydedup":
+                train_filter_fun = lambda r: (r['Name'], r['Exename']) in library_func_names_dedup
+            else:
+                assert False, "Invalid configuration"
+
             return [
                 datasets.SplitGenerator(
                     name="train",
                     gen_kwargs={
-                        "ds": ds.filter(lambda r: r['Name'] in library_func_names),
+                        "ds": ds.filter(train_filter_fun),
                     },
                 ),
                 datasets.SplitGenerator(