Add first loading script that has configs for splits

oo-method-test-split.py

@@ -0,0 +1,112 @@
+#!/usr/bin/python
+
+import datasets
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+BASE_DATASET = "ejschwartz/oo-method-test"
+
+class OOMethodTestDataset(datasets.ArrowBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="combined",
+            version=datasets.Version("1.0.0"),
+            description="All data files combined",
+        ),
+        datasets.BuilderConfig(
+            name="byrow",
+            version=datasets.Version("1.0.0"),
+            description="Split by example (dumb)",
+        ),
+        datasets.BuilderConfig(
+            name="byfuncname",
+            version=datasets.Version("1.0.0"),
+            description="Split by function name",
+        )
+
+    ]
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _info(self):
+        return datasets.DatasetInfo()
+
+    def _split_generators(self, dl_manager):
+        ds = datasets.load_dataset(BASE_DATASET)
+
+        #print(files)
+        #print(downloaded_files)
+
+        if self.config.name == "combined":
+
+            return [
+                datasets.SplitGenerator(
+                    name="combined",
+                    gen_kwargs={
+                        "ds": ds['combined'],
+                    },
+                ),
+            ]
+        
+        elif self.config.name == "byrow":
+
+            ds = ds['combined'].train_test_split(test_size=0.1, seed=42)
+            #print(ds)
+
+            return [
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={
+                        "ds": ds['train'],
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test",
+                    gen_kwargs={
+                        "ds": ds['test'],
+                    },
+                ),
+
+            ]
+        
+        elif self.config.name == "byfuncname":
+
+            ds = ds['combined']
+
+            unique_names = ds.unique('Name')
+            nameds = datasets.Dataset.from_dict({'Name': unique_names})
+
+            name_split = nameds.train_test_split(test_size=0.1, seed=42)
+            #print(name_split)
+
+            train_name = name_split['train']['Name']
+            test_name = name_split['test']['Name']
+
+            return [
+                datasets.SplitGenerator(
+                    name="train",
+                    gen_kwargs={
+                        "ds": ds.filter(lambda r: r['Name'] in train_name),
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name="test",
+                    gen_kwargs={
+                        "ds": ds.filter(lambda r: r['Name'] in test_name),
+                    },
+                ),
+
+            ]
+
+        else:
+            assert False
+    
+    def _generate_tables(self, ds):
+
+        # Converting to pandas is silly, but the old version of datasets doesn't
+        # seem to have a way to convert to Arrow?
+        for i, batch in enumerate(ds.to_pandas(batched=True)):
+            yield i, pa.Table.from_pandas(batch)