Create FIREBALL.py

FIREBALL.py

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+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import jsonlines
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{Zhu2023FIREBALL,
+title={{FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information}},
+author={Zhu, Andrew and Aggarwal, Karmanya and Feng, Alexander and Martin, Lara J. and Callison-Burch, Chris},
+year={2023},
+booktitle={Annual Meeting of the Association for Computational Linguistics (ACL)},
+month={7},
+url={https://aclanthology.org/2023.acl-long.229/},
+address={Toronto, Canada},
+pages={4171--4193},
+publisher={ACL},
+doi={10.18653/v1/2023.acl-long.229}
+}
+"""
+
+
+_DESCRIPTION = """\
+FIREBALL Dungeons & Dragons data with narrative and Avrae scripting commands.
+"""
+
+_HOMEPAGE = "https://github.com/zhudotexe/FIREBALL"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "cc-by-4.0"
+
+
+# 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)
+_URLS = {
+    "FIREBALL": "https://huggingface.co/datasets/lara-martin/FIREBALL/tree/main/filtered",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # 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="FIREBALL", version=VERSION, description="This part of my dataset covers a first domain"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "FIREBALL"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        
+        features = datasets.Features(
+            {
+                "speaker_id": datasets.Value('int64'),
+                "before_utterances": datasets.Sequence(datasets.Value('string')), 
+                'combat_state_before': datasets.Sequence(
+                    { 
+                    'name': datasets.Value(dtype='string'),
+                    'hp': datasets.Value(dtype='string'),
+                    'class': datasets.Value(dtype='string'),
+                    'race': datasets.Value(dtype='string'),
+                    'attacks': datasets.Value(dtype='string'),
+                    'spells': datasets.Value(dtype='string'),
+                    'actions': datasets.Value(dtype='string'),
+                    'effects': datasets.Value(dtype='string'),
+                    'description': datasets.Value(dtype='string'),
+                    'controller_id': datasets.Value(dtype='string')
+                    }
+                ), #list of dictionaries
+                'current_actor': { 
+                    'name': datasets.Value(dtype='string'),
+                    'hp': datasets.Value(dtype='string'),
+                    'class': datasets.Value(dtype='string'),
+                    'race': datasets.Value(dtype='string'),
+                    'attacks': datasets.Value(dtype='string'),
+                    'spells': datasets.Value(dtype='string'),
+                    'actions': datasets.Value(dtype='string'),
+                    'effects': datasets.Value(dtype='string'),
+                    'description': datasets.Value(dtype='string'),
+                    'controller_id': datasets.Value(dtype='string')
+                }, #dictionary
+                'commands_norm': datasets.value('string'),
+                'automation_results': datasets.value('string'),
+                'caster_after': {
+                    'name': datasets.Value(dtype='string'),
+                    'hp': datasets.Value(dtype='string'),
+                    'class': datasets.Value(dtype='string'),
+                    'race': datasets.Value(dtype='string'),
+                    'attacks': datasets.Value(dtype='string'),
+                    'spells': datasets.Value(dtype='string'),
+                    'actions': datasets.Value(dtype='string'),
+                    'effects': datasets.Value(dtype='string'),
+                    'description': datasets.Value(dtype='string'),
+                    'controller_id': datasets.Value(dtype='string')
+                }, #dictionary
+                'targets_after': datasets.Sequence(
+                    { 
+                    'name': datasets.Value(dtype='string'),
+                    'hp': datasets.Value(dtype='string'),
+                    'class': datasets.Value(dtype='string'),
+                    'race': datasets.Value(dtype='string'),
+                    'attacks': datasets.Value(dtype='string'),
+                    'spells': datasets.Value(dtype='string'),
+                    'actions': datasets.Value(dtype='string'),
+                    'effects': datasets.Value(dtype='string'),
+                    'description': datasets.Value(dtype='string'),
+                    'controller_id': datasets.Value(dtype='string')
+                    }
+                ), #list of dictionaries
+                'combat_state_after': datasets.Sequence(
+                    { 
+                    'name': datasets.Value(dtype='string'),
+                    'hp': datasets.Value(dtype='string'),
+                    'class': datasets.Value(dtype='string'),
+                    'race': datasets.Value(dtype='string'),
+                    'attacks': datasets.Value(dtype='string'),
+                    'spells': datasets.Value(dtype='string'),
+                    'actions': datasets.Value(dtype='string'),
+                    'effects': datasets.Value(dtype='string'),
+                    'description': datasets.Value(dtype='string'),
+                    'controller_id': datasets.Value(dtype='string')
+                    }
+                ), #list of dictionaries
+                'after_utterances': datasets.Sequence(datasets.Value('string')), 
+                'utterance_history': datasets.Sequence(datasets.Value('string')),
+                'before_idxs': datasets.Sequence(datasets.Value('int16')),
+                'before_state_idx': datasets.Value('int16'),
+                'command_idxs': datasets.Sequence(datasets.Value('int16')),
+                'after_state_idx': datasets.Value('int16'),
+                'after_idxs': datasets.Sequence(datasets.Value('int16')),
+                'embed_idxs': datasets.Sequence(datasets.Value('int16'))
+            }
+        )
+        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
+            features=features,  # Here we define them above because they are different between the two configurations
+            # 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
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    # def _split_generators(self, dl_manager):
+    #     # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+    #     # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+    #     # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+    #     # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+    #     # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+    #     urls = _URLS[self.config.name]
+    #     data_dir = dl_manager.download_and_extract(urls)
+    #     for root,dirs,files in os.walk(data_dir):
+    #         for f in files:
+    #             data = os.path.join()
+    #     return [
+    #         datasets.SplitGenerator(
+    #             name=datasets.Split.TRAIN,
+    #             # These kwargs will be passed to _generate_examples
+    #             gen_kwargs={
+    #                 "filepath": os.path.join(data_dir, "train.jsonl"),
+    #                 "split": "train",
+    #             },
+    #         ),
+    #     ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        key = 0
+        for root,dirs,files in os.walk(filepath):
+          for file in files:
+            with jsonlines.open(os.path.join(root,file)) as f:
+              for data in f:
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "speaker_id": data["speaker_id"],
+                    "before_utterances": data["before_utterances"], 
+                    'combat_state_before': data['combat_state_before'],
+                    'current_actor': data["current_actor"],
+                    'commands_norm': data['commands_norm'],
+                    'automation_results': data['automation_results'],
+                    'caster_after': data['caster_after'],
+                    'targets_after': data['targets_after'],
+                    'combat_state_after': data['combat_state_after'],
+                    'after_utterances': data['after_utterances'],
+                    'utterance_history': data['utterance_history'],
+                    'before_idxs': data['before_idxs'],
+                    'before_state_idx': data['before_state_idx'],
+                    'command_idxs': data['command_idxs'],
+                    'after_state_idx': data['after_state_idx'],
+                    'after_idxs': data['after_idxs'],
+                    'embed_idxs': data['embed_idxs']
+                }
+                key+=1