add sperate train and test splits

quakeflow_nc.py

@@ -52,7 +52,7 @@ _LICENSE = ""
 # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _REPO = "https://huggingface.co/datasets/AI4EPS/quakeflow_nc/resolve/main/data"
-_FILENAMES = [
+_FILES = [
     "NC1970-1989.h5",
     "NC1990-1994.h5",
     "NC1995-1999.h5",
@@ -70,10 +70,13 @@ _FILENAMES = [
     "NC2019.h5",
     "NC2020.h5",
 ]
-# _FILENAMES = ["NC2020.h5"]
 _URLS = {
-    "station": [f"{_REPO}/{x}" for x in _FILENAMES],
-    "event": [f"{_REPO}/{x}" for x in _FILENAMES],
+    "station": [f"{_REPO}/{x}" for x in _FILES],
+    "event": [f"{_REPO}/{x}" for x in _FILES],
+    "station_train": [f"{_REPO}/{x}" for x in _FILES[:-1]],
+    "event_train": [f"{_REPO}/{x}" for x in _FILES[:-1]],
+    "station_test": [f"{_REPO}/{x}" for x in _FILES[-1:]],
+    "event_test": [f"{_REPO}/{x}" for x in _FILES[-1:]],
 }
 
 
@@ -117,17 +120,39 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
 
     # default config, you can change batch_size and num_stations_list when use `datasets.load_dataset`
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="station", version=VERSION, description="yield station-based samples one by one"),
-        datasets.BuilderConfig(name="event", version=VERSION, description="yield event-based samples one by one"),
+        datasets.BuilderConfig(
+            name="station", version=VERSION, description="yield station-based samples one by one of whole dataset"
+        ),
+        datasets.BuilderConfig(
+            name="event", version=VERSION, description="yield event-based samples one by one of whole dataset"
+        ),
+        datasets.BuilderConfig(
+            name="station_train",
+            version=VERSION,
+            description="yield station-based samples one by one of training dataset",
+        ),
+        datasets.BuilderConfig(
+            name="event_train", version=VERSION, description="yield event-based samples one by one of training dataset"
+        ),
+        datasets.BuilderConfig(
+            name="station_test", version=VERSION, description="yield station-based samples one by one of test dataset"
+        ),
+        datasets.BuilderConfig(
+            name="event_test", version=VERSION, description="yield event-based samples one by one of test dataset"
+        ),
     ]
 
     DEFAULT_CONFIG_NAME = (
-        "station"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+        "station_test"  # It's not mandatory to have a default configuration. Just use one if it make sense.
     )
 
     def _info(self):
         # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "station":
+        if (
+            (self.config.name == "station")
+            or (self.config.name == "station_train")
+            or (self.config.name == "station_test")
+        ):
             features = datasets.Features(
                 {
                     "waveform": datasets.Array2D(shape=(3, self.nt), dtype="float32"),
@@ -137,7 +162,7 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                 }
             )
 
-        elif self.config.name == "event":
+        elif (self.config.name == "event") or (self.config.name == "event_train") or (self.config.name == "event_test"):
             features = datasets.Features(
                 {
                     "waveform": datasets.Array3D(shape=(None, 3, self.nt), dtype="float32"),
@@ -173,31 +198,42 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
         urls = _URLS[self.config.name]
         # files = dl_manager.download(urls)
         files = dl_manager.download_and_extract(urls)
-        # files = ["./data/ncedc_event_dataset_000.h5"]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": files[:-1],
-                    "split": "train",
-                },
-            ),
-            # datasets.SplitGenerator(
-            #     name=datasets.Split.VALIDATION,
-            #     # These kwargs will be passed to _generate_examples
-            #     gen_kwargs={
-            #         "filepath": os.path.join(data_dir, "dev.jsonl"),
-            #         "split": "dev",
-            #     },
-            # ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": files[-1:], "split": "test"},
-            ),
-        ]
+        print(files)
+
+        if self.config.name == "station" or self.config.name == "event":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": files[:-1],
+                        "split": "train",
+                    },
+                ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": files[-1:], "split": "test"},
+                ),
+            ]
+        elif self.config.name == "station_train" or self.config.name == "event_train":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": files,
+                        "split": "train",
+                    },
+                ),
+            ]
+        elif self.config.name == "station_test" or self.config.name == "event_test":
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": files, "split": "test"},
+                ),
+            ]
+        else:
+            raise ValueError("config.name is not in BUILDER_CONFIGS")
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
     def _generate_examples(self, filepath, split):
@@ -212,7 +248,11 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                     for event_id in event_ids:
                         event = fp[event_id]
                         station_ids = list(event.keys())
-                        if self.config.name == "station":
+                        if (
+                            (self.config.name == "station")
+                            or (self.config.name == "station_train")
+                            or (self.config.name == "station_test")
+                        ):
                             waveforms = np.zeros([3, self.nt], dtype="float32")
                             phase_pick = np.zeros_like(waveforms)
                             attrs = event.attrs
@@ -239,7 +279,11 @@ class QuakeFlow_NC(datasets.GeneratorBasedBuilder):
                                     "station_location": torch.from_numpy(np.array(station_location)).float(),
                                 }
 
-                        elif self.config.name == "event":
+                        elif (
+                            (self.config.name == "event")
+                            or (self.config.name == "event_train")
+                            or (self.config.name == "event_test")
+                        ):
                             waveforms = np.zeros([len(station_ids), 3, self.nt], dtype="float32")
                             phase_pick = np.zeros_like(waveforms)
                             attrs = event.attrs