Add config script

OCW.py

@@ -0,0 +1,233 @@
+# 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.
+
+"""Only Connect Wall (OCW) dataset"""
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{Naeini2023LargeLM,
+    title        = {Large Language Models are Fixated by Red Herrings: Exploring Creative Problem Solving and Einstellung Effect using the Only Connect Wall Dataset},
+    author       = {Saeid Alavi Naeini and Raeid Saqur and Mozhgan Saeidi and John Giorgi and Babak Taati},
+    year         = 2023,
+    journal      = {ArXiv},
+    volume       = {abs/2306.11167},
+    url          = {https://api.semanticscholar.org/CorpusID:259203717}
+}
+"""
+
+_DESCRIPTION = """\
+The Only Connect Wall (OCW) dataset contains 618 "Connecting Walls" from the Round 3: Connecting Wall segment of the Only Connect quiz show, collected from 15 seasons' worth of episodes. Each wall contains the ground-truth groups and connections as well as recorded human performance.
+"""
+
+_HOMEPAGE_URL = "https://github.com/TaatiTeam/OCW/"
+
+_LICENSE = "MIT"
+
+_BASE_URL = "https://huggingface.co/datasets/TaatiTeam/OCW/resolve/main/"
+_URLS = {
+    "ocw_train": _BASE_URL + "train.json",
+    "ocw_validation": _BASE_URL + "validation.json",
+    "ocw_test": _BASE_URL + "test.json",
+    "ocw_randomized_test": _BASE_URL + "easy_test_randomized.json",
+    "ocw_wordnet_train": _BASE_URL + "easy_train_wordnet.json",
+    "ocw_wordnet_validation": _BASE_URL + "easy_validation_wordnet.json",
+    "ocw_wordnet_test": _BASE_URL + "easy_test_wordnet.json"
+}
+
+
+class OCW(datasets.GeneratorBasedBuilder):
+    """OCW dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="ocw", version=VERSION,
+                               description="main OCW dataset"),
+        datasets.BuilderConfig(name="ocw_randomized", version=VERSION,
+                               description="Easy OCW dataset with randomized groups in each wall"),
+        datasets.BuilderConfig(name="ocw_wordnet", version=VERSION,
+                               description="Easy OCW dataset with wordnet synonyms replaced with original clues")
+    ]
+
+    DEFAULT_CONFIG_NAME = "ocw"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                # "total_walls_in_season": datasets.Value("int32"),
+                # "season_start_date": datasets.Value("string"),
+                # "season_end_date": datasets.Value("string"),
+                "wall_id": datasets.Value("string"),
+                "season": datasets.Value("int32"),
+                "episode": datasets.Value("int32"),
+                "words": datasets.Value("string"),
+                "gt_connections": datasets.Value("string"),
+                "group_1":
+                    {
+                        "group_id": datasets.Value("string"),
+                        "gt_words": datasets.Value("string"),
+                        "gt_connection": datasets.Value("string"),
+                        "human_performance":
+                            {
+                            "grouping": datasets.Value("int32"),
+                            "connection": datasets.Value("int32")
+                            }
+                    },
+                "group_2":
+                    {
+                        "group_id": datasets.Value("string"),
+                        "gt_words": datasets.Value("string"),
+                        "gt_connection": datasets.Value("string"),
+                        "human_performance": 
+                            {
+                            "grouping": datasets.Value("int32"),
+                            "connection": datasets.Value("int32")
+                            }
+                    },
+                "group_3":
+                    {
+                        "group_id": datasets.Value("string"),
+                        "gt_words": datasets.Value("string"),
+                        "gt_connection": datasets.Value("string"),
+                        "human_performance":
+                            {
+                            "grouping": datasets.Value("int32"),
+                            "connection": datasets.Value("int32")
+                            }
+                    },
+                "group_4":
+                    {
+                        "group_id": datasets.Value("string"),
+                        "gt_words": datasets.Value("string"),
+                        "gt_connection": datasets.Value("string"),
+                        "human_performance":
+                            {
+                            "grouping": datasets.Value("int32"),
+                            "connection": datasets.Value("int32")
+                            }
+                    },
+
+            }
+        )
+        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,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE_URL,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+            # No default supervised_keys
+            supervised_keys=None
+        )
+
+    def _split_generators(self, dl_manager):
+        test_url = _URLS[self.config.name + '_test']
+        # only test set is randomized for ablation studies
+        if self.config.name == 'ocw_randomized':
+            train_url = _URLS['ocw_train']
+            validation_url = _URLS['ocw_validation']
+        else:
+            train_url = _URLS[self.config.name + '_train']
+            validation_url = _URLS[self.config.name + '_validation']
+
+        train_path = dl_manager.download_and_extract(train_url)
+        validation_path = dl_manager.download_and_extract(validation_url)
+        test_path = dl_manager.download_and_extract(test_url)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            ocw = json.load(f)
+            for data in ocw["dataset"]:
+                wall_id = data.get("wall_id")
+                season = data.get("season")
+                # season_to_walls_map = ocw['season_to_walls_map'][str(season)]
+                # total_walls_in_season = season_to_walls_map["num_walls"]
+                # season_start_date = season_to_walls_map["start_date"]
+                # season_end_date = season_to_walls_map["end_date"]
+                episode = data.get("episode")
+                words = data.get("words")
+                gt_connections = data.get("gt_connections")
+                group_1 = data['groups']['group_1']
+                group_1_human_performance = group_1['human_performance']
+                group_2 = data['groups']['group_2']
+                group_2_human_performance = group_2['human_performance']
+                group_3 = data['groups']['group_3']
+                group_3_human_performance = group_3['human_performance']
+                group_4 = data['groups']['group_4']
+                group_4_human_performance = group_4['human_performance']
+                yield key, {
+                    # "total_walls_in_season": total_walls_in_season,
+                    # "season_start_date": season_start_date,
+                    # "season_end_date": season_end_date,
+                    "wall_id": wall_id,
+                    "season": season,
+                    "episode": episode,
+                    "words": words,
+                    "gt_connections": gt_connections,
+                    "group_1": {
+                        "group_id": group_1.get("group_id"),
+                        "gt_words": group_1.get("gt_words"),
+                        "gt_connection": group_1.get("gt_connection"),
+                        "human_performance": {
+                            "grouping": group_1_human_performance.get("grouping"),
+                            "connection": group_1_human_performance.get("connection")
+                        }
+                    },
+                    "group_2": {
+                        "group_id": group_2.get("group_id"),
+                        "gt_words": group_2.get("gt_words"),
+                        "gt_connection": group_2.get("gt_connection"),
+                        "human_performance": {
+                            "grouping": group_2_human_performance.get("grouping"),
+                            "connection": group_2_human_performance.get("connection")
+                        }
+                    },
+                    "group_3": {
+                        "group_id": group_3.get("group_id"),
+                        "gt_words": group_3.get("gt_words"),
+                        "gt_connection": group_3.get("gt_connection"),
+                        "human_performance": {
+                            "grouping": group_3_human_performance.get("grouping"),
+                            "connection": group_3_human_performance.get("connection")
+                        }
+                    },
+                    "group_4": {
+                        "group_id": group_4.get("group_id"),
+                        "gt_words": group_4.get("gt_words"),
+                        "gt_connection": group_4.get("gt_connection"),
+                        "human_performance": {
+                            "grouping": group_4_human_performance.get("grouping"),
+                            "connection": group_4_human_performance.get("connection")
+                        }
+                    },
+                }
+                key += 1