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import pickle
import datasets
import numpy as np
_DESCRIPTION = """The dataset consists of tuples of (observations, actions, rewards, dones) sampled by agents
interacting with the CityLearn 2022 Phase 1 environment"""
_BASE_URL = "https://huggingface.co/datasets/TobiTob/CityLearn/resolve/main"
_URLS = {
"s_test": f"{_BASE_URL}/s_test.pkl",
"s_test2": f"{_BASE_URL}/s_test2.pkl",
"s_test3": f"{_BASE_URL}/s_test3.pkl",
"s_test4": f"{_BASE_URL}/s_test4.pkl",
"s_test5": f"{_BASE_URL}/s_test5.pkl",
"s_test6": f"{_BASE_URL}/s_test6.pkl",
"s_test7": f"{_BASE_URL}/s_test7.pkl",
"s_8759x5": f"{_BASE_URL}/s_8759x5.pkl",
"halfcheetah-medium-replay-v2": f"{_BASE_URL}/test.pkl",
}
class DecisionTransformerCityLearnDataset(datasets.GeneratorBasedBuilder):
# 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",
description="Test Data size 10x8",
),
datasets.BuilderConfig(
name="s_test2",
description="Test Data size 10x16",
),
datasets.BuilderConfig(
name="s_test3",
description="Test Data size 100x16",
),
datasets.BuilderConfig(
name="s_test4",
description="Test Data size 10x1000",
),
datasets.BuilderConfig(
name="s_test5",
description="Test Data size 260x168 (week)",
),
datasets.BuilderConfig(
name="s_test6",
description="Test Data size 60x730 (month)",
),
datasets.BuilderConfig(
name="s_test7",
description="Test Data size 5x3000",
),
datasets.BuilderConfig(
name="s_8759x5",
description="Data sampled from an expert policy in CityLearn environment",
),
datasets.BuilderConfig(
name="halfcheetah-medium-replay-v2",
description="Test Data",
),
]
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")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
)
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)
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),
}
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