Add bylibrary

oo-method-test-split.py

@@ -2,11 +2,19 @@
 
 import datasets
 
+import itertools
+
+import os
+
 import pyarrow as pa
 import pyarrow.parquet as pq
 
 BASE_DATASET = "ejschwartz/oo-method-test"
 
+def setexe(r):
+    r['Dirname'], r['Exename'] = os.path.split(r['Binary'])
+    return r
+
 class OOMethodTestDataset(datasets.ArrowBasedBuilder):
 
     BUILDER_CONFIGS = [
@@ -24,6 +32,11 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
             name="byfuncname",
             version=datasets.Version("1.0.0"),
             description="Split by function name",
+        ),
+        datasets.BuilderConfig(
+            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",
         )
 
     ]
@@ -35,25 +48,28 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
         return datasets.DatasetInfo()
 
     def _split_generators(self, dl_manager):
-        ds = datasets.load_dataset(BASE_DATASET)
+        ds = datasets.load_dataset(BASE_DATASET)['combined']
 
         #print(files)
         #print(downloaded_files)
 
+
+        ds = ds.map(setexe, batched=False)
+
         if self.config.name == "combined":
 
             return [
                 datasets.SplitGenerator(
                     name="combined",
                     gen_kwargs={
-                        "ds": ds['combined'],
+                        "ds": ds,
                     },
                 ),
             ]
         
         elif self.config.name == "byrow":
 
-            ds = ds['combined'].train_test_split(test_size=0.1, seed=42)
+            ds = ds.train_test_split(test_size=0.1, seed=42)
             #print(ds)
 
             return [
@@ -74,8 +90,6 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
         
         elif self.config.name == "byfuncname":
 
-            ds = ds['combined']
-
             unique_names = ds.unique('Name')
             nameds = datasets.Dataset.from_dict({'Name': unique_names})
 
@@ -100,6 +114,39 @@ class OOMethodTestDataset(datasets.ArrowBasedBuilder):
                 ),
 
             ]
+        
+        elif self.config.name == "bylibrary":
+            # A function (name) is a library function if it appears in more than one Exename
+
+            # this is (('func', 'oo.exe'): 123)
+            testcount = set(zip(ds['Name'], ds['Exename']))
+
+            # sorted pairs by function name
+            testcount = sorted(testcount, key=lambda x: x[0])
+
+            # group by function name
+            grouped = itertools.groupby(testcount, lambda t: t[0])
+
+            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}
+            nonlibrary_func_names = {f for f, exes in grouped.items() if len(exes) == 1}
+
+            return [
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={
+                        "ds": ds.filter(lambda r: r['Name'] in library_func_names),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test",
+                    gen_kwargs={
+                        "ds": ds.filter(lambda r: r['Name'] in nonlibrary_func_names),
+                    },
+                ),
+
+            ]
 
         else:
             assert False