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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_8759x5": f"{_BASE_URL}/s_8759x5.pkl",
"test": f"{_BASE_URL}/test.pkl",
}
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')
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="s_test",
description="Test Data sampled from an expert policy in CityLearn environment",
),
datasets.BuilderConfig(
name="s_8759x5",
description="Test Data sampled from an expert policy in CityLearn environment",
),
datasets.BuilderConfig(
name="test",
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)
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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