File size: 3,552 Bytes
b33ee2f 8c8ab30 6760541 8c8ab30 67508ed b33ee2f 8e3eacd 1653a2b b33ee2f 890f548 0a2304d b33ee2f 8e3eacd 6760541 8e3eacd dfb8e6c 1653a2b dfb8e6c 89b7302 1653a2b 89b7302 b33ee2f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
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 (only first 5 buildings)"""
_BASE_URL = "https://huggingface.co/datasets/TobiTob/CityLearn/resolve/main"
_URLS = {
"f_230": f"{_BASE_URL}/f_230x5x38.pkl",
"LSTM": f"{_BASE_URL}/L_2189x5x4.pkl",
"RB1": f"{_BASE_URL}/R1_2189x5x4.pkl",
"RB2": f"{_BASE_URL}/R2_2189x5x4.pkl",
"Merged1": f"{_BASE_URL}/L_R1_2189x5x8.pkl",
"Merged2": f"{_BASE_URL}/L_R1_R2_2189x5x12.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="f_230",
description="Data sampled from an expert LSTM policy. Sequence length = 230, Buildings = 5, Episodes = 1 ",
),
datasets.BuilderConfig(
name="LSTM",
description="Data sampled from an expert LSTM policy. Sequence length = 2189, Buildings = 5, Episodes = 1 ",
),
datasets.BuilderConfig(
name="RB1",
description="Data sampled a rule based policy. Sequence length = 2189, Buildings = 5, Episodes = 1 ",
),
datasets.BuilderConfig(
name="RB2",
description="Data sampled a rule based policy. Sequence length = 2189, Buildings = 5, Episodes = 1 ",
),
datasets.BuilderConfig(
name="Merged1",
description="LSTM + RBC1. Sequence length = 2189, Buildings = 5, Episodes = 1+1 ",
),
datasets.BuilderConfig(
name="Merged2",
description="LSTM + RBC1 + RBC2. Sequence length = 2189, Buildings = 5, Episodes = 1+1+1 ",
),
]
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),
}
|