Update CityLearn.py

CityLearn.py

@@ -14,20 +14,9 @@ _URLS = {
 
 
 class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
-    """The dataset comprises of tuples of (Observations, Actions, Rewards, Dones) sampled
-    by agents interacting with the CityLearn 2022 phase 1 environment"""
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    
+    # You will be able to load one configuration in the following list with
+    # data = datasets.load_dataset('TobiTob/CityLearn', 'data_name')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
             name="s_test",
@@ -51,20 +40,12 @@ class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
                 "actions": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
                 "rewards": datasets.Sequence(datasets.Value("float32")),
                 "dones": datasets.Sequence(datasets.Value("bool")),
-                # These are the features of your dataset like images, labels ...
             }
         )
 
         return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
             description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            # Here we define them above because they are different between the two configurations
             features=features,
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
         )
 
     def _split_generators(self, dl_manager):