nld-aa-taster.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

""" Dungeons and Data: A Large-Scale NetHack Dataset. """

import glob
import h5py
import json
import os
import datasets

from datasets.download.streaming_download_manager import xopen

_CITATION = """\
"""

_DESCRIPTION = """\
3 billion state-action-score transitions from 100,000 trajectories collected from the symbolic bot winner of the NetHack Challenge 2021.
"""

_HOMEPAGE = ""

_LICENSE = ""


class NLEDataset(datasets.GeneratorBasedBuilder):
    """Dungeons and Data: A Large-Scale NetHack Dataset."""
    VERSION = datasets.Version("1.0.0")
    
    DEFAULT_CONFIG_NAME = "mon-hum-neu"

    def _info(self):
        features = datasets.Features(
            {
                "data": {
                    "tty_chars": datasets.Array3D(shape=(None, 24, 80), dtype="uint8"),
                    "tty_colors": datasets.Array3D(shape=(None, 24, 80), dtype="int8"),
                    "tty_cursor": datasets.Array2D(shape=(None, 2), dtype="int16"),
                    "actions": datasets.Sequence(datasets.Value("int16")),
                    "rewards": datasets.Sequence(datasets.Value("int32")),
                    "dones": datasets.Sequence(datasets.Value("bool")),
            },
                "metadata": {
                    "gameid": datasets.Value("int32"), 
                    "version": datasets.Value("string"), 
                    "points": datasets.Value("int32"), 
                    "deathdnum": datasets.Value("int32"), 
                    "deathlev": datasets.Value("int32"), 
                    "maxlvl": datasets.Value("int32"), 
                    "hp": datasets.Value("int32"), 
                    "maxhp": datasets.Value("int32"), 
                    "deaths": datasets.Value("int32"), 
                    "deathdate": datasets.Value("int32"), 
                    "birthdate": datasets.Value("int32"), 
                    "uid": datasets.Value("int32"), 
                    "role": datasets.Value("string"), 
                    "race": datasets.Value("string"), 
                    "gender": datasets.Value("string"), 
                    "align": datasets.Value("string"), 
                    "name": datasets.Value("string"), 
                    "death": datasets.Value("string"), 
                    "conduct": datasets.Value("string"), 
                    "turns": datasets.Value("int32"), 
                    "achieve": datasets.Value("string"), 
                    "realtime": datasets.Value("int64"), 
                    "starttime": datasets.Value("int64"), 
                    "endtime": datasets.Value("int64"), 
                    "gender0": datasets.Value("string"), 
                    "align0": datasets.Value("string"), 
                    "flags": datasets.Value("string")
                }
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # data_file = dl_manager.download_and_extract(f"data/data-{self.config.name}-any.hdf5.zip")
        data_file = dl_manager.download(f"data/data-{self.config.name}-any.hdf5")
        metadata_file = dl_manager.download(f"data/metadata-{self.config.name}-any.json")
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN, 
                gen_kwargs={
                    "data_file": data_file,
                    "metadata_file": metadata_file, 
                    "dl_manager": dl_manager
                }
            )
        ]

    def _generate_examples(self, data_file, metadata_file, dl_manager):
        if dl_manager.is_streaming:
            data_file = xopen(data_file, "rb")

        with h5py.File(data_file, "r") as df, xopen(metadata_file, "r") as f:
            # this thing is super small, so we will load it all
            meta = json.load(f)

            for i, (ep_key, ep_meta) in enumerate(zip(df["/"], meta)):
                assert int(ep_key) == int(ep_meta["gameid"])

                yield i, {
                    "data": {
                        "tty_chars": df[f"{ep_key}/tty_chars"][()],
                        "tty_colors": df[f"{ep_key}/tty_colors"][()],
                        "tty_cursor": df[f"{ep_key}/tty_cursor"][()],
                        "actions": df[f"{ep_key}/actions"][()],
                        "rewards": df[f"{ep_key}/rewards"][()],
                        "dones": df[f"{ep_key}/dones"][()]
                    },
                    "metadata": ep_meta
                }

        if dl_manager.is_streaming:
            data_file.close()