Refactor script

top_quark_tagging.py

@@ -29,18 +29,27 @@ _TEST_DOWNLOAD_URL = "data/test-raw.parquet"
 _URLS = {
     "raw": {
         "train": "data/train-raw.parquet",
+        "train-labels": "data/train-labels.parquet",
         "validation": "data/validation-raw.parquet",
+        "validation-labels": "data/validation-labels.parquet",
         "test": "data/test-raw.parquet",
+        "test-labels": "data/test-labels.parquet",
     },
     "nsubjettiness": {
         "train": "data/train-nsubjettiness.parquet",
+        "train-labels": "data/train-labels.parquet",
         "validation": "data/validation-nsubjettiness.parquet",
+        "validation-labels": "data/validation-labels.parquet",
         "test": "data/test-nsubjettiness.parquet",
+        "test-labels": "data/test-labels.parquet",
     },
     "image": {
         "train": "data/train-images.parquet",
+        "train-labels": "data/train-labels.parquet",
         "validation": "data/train-images.parquet",
+        "validation-labels": "data/validation-labels.parquet",
         "test": "data/train-images.parquet",
+        "test-labels": "data/test-labels.parquet",
     },
 }
 
@@ -896,23 +905,30 @@ class TopQuarkTagging(datasets.GeneratorBasedBuilder):
         data_paths = dl_manager.download_and_extract(urls)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_paths["train"], "split": "train"}
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": [data_paths["train"], data_paths["train-labels"]], "split": "train"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                gen_kwargs={"filepath": data_paths["validation"], "split": "validation"},
+                gen_kwargs={
+                    "filepath": [data_paths["validation"], data_paths["validation-labels"]],
+                    "split": "validation",
+                },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": data_paths["test"], "split": "test"}
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": [data_paths["test"], data_paths["test-labels"]], "split": "test"},
             ),
         ]
 
     def _generate_examples(self, filepath, split):
         """Generate examples."""
-        ds = datasets.load_dataset("parquet", data_files={split: filepath}, split=split)
-        for id_, row in enumerate(ds):
+        examples = datasets.load_dataset("parquet", data_files={split: filepath[0]}, split=split)
+        labels = datasets.load_dataset("parquet", data_files={split: filepath[1]}, split=split)
+        for id_, (row, label) in enumerate(zip(examples, labels)):
             if self.config.name == "image":
-                image = np.array(row[:-1].reshape(33, 33))
-                yield id_, {"image": image, "is_signal_new": row[-1]}
+                values = np.fromiter(row.values(), dtype=float)
+                image = values.reshape(33, 33)
+                yield id_, {"image": image, **label}
             else:
-                yield id_, row
+                yield id_, {**row, **label}