Delete CityLearn.py

CityLearn.py

@@ -1,94 +0,0 @@
-import pickle
-
-import datasets
-import numpy as np
-
-_DESCRIPTION = """\
-This dataset is used to train a decision Transformer for the CityLearn 2022 environment https://www.aicrowd.com/challenges/neurips-2022-citylearn-challenge
-"""
-
-# 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)
-_BASE_URL = "https://huggingface.co/datasets/TobiTob/CityLearn/resolve/main"
-_URLS = {
-    "sequences": f"{_BASE_URL}/sequences.pkl",
-}
-
-
-class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
-    """The dataset comprises of tuples of (Observations, Actions, Rewards, Dones) sampled
-    by an expert policy for various continuous control tasks"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # 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')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="sequences",
-            version=VERSION,
-            description="Test Data sampled from an expert policy in CityLearn environment",
-        ),
-    ]
-
-    def _info(self):
-
-        features = datasets.Features(
-            {
-                "observations": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))),
-                "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):
-        urls = _URLS[self.config.name]
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir,
-                    "split": "train",
-                },
-            )
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, "rb") as f:
-            trajectories = pickle.load(f)
-            print(trajectories)
-            print(type(trajectories))
-
-            for idx, traj in enumerate(trajectories):
-                yield idx, {
-                    "observations": traj["observations"],
-                    "actions": traj["actions"],
-                    "rewards": np.expand_dims(traj["rewards"], axis=1),
-                    "dones": np.expand_dims(traj.get("dones", traj.get("terminals")), axis=1),
-                }