agieval.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.
"""LogiQA dataset."""

import datasets
import json
import ast
import pandas as pd
import csv

_CITATION = """\
@ARTICLE{10174688,
  author={Liu, Hanmeng and Liu, Jian and Cui, Leyang and Teng, Zhiyang and Duan, Nan and Zhou, Ming and Zhang, Yue},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  title={LogiQA 2.0 — An Improved Dataset for Logical Reasoning in Natural Language Understanding},
  year={2023},
  volume={},
  number={},
  pages={1-16},
  doi={10.1109/TASLP.2023.3293046}}
"""

_HOMEPAGE = "https://github.com/csitfun/LogiQA2.0/tree/main"

_LICENSE = (
    "Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License"
)
HEAD = 'https://raw.githubusercontent.com/microsoft/AGIEval/main/data/v1/'

_DESCRIPTION = "AGIEval is a human-centric benchmark specifically designed to evaluate the general abilities of foundation models in tasks pertinent to human cognition and problem-solving. This benchmark is derived from 20 official, public, and high-standard admission and qualification exams intended for general human test-takers, such as general college admission tests"

_URLS = {
    "sat_en": {
        "test": HEAD + 'sat-en.jsonl',
    },
    "sat_en_wop": {
        "test": HEAD + 'sat-en-without-passage.jsonl',
    },
    "sat_math": {
        "test": HEAD + 'sat-math.jsonl'
    },
    "lsat_ar": {
        "test": HEAD + 'lsat-ar.jsonl'
    },
    "lsat_lr": {
        "test": HEAD + 'lsat-lr.jsonl'
    },
    "lsat_rc": {
        "test": HEAD + 'lsat-rc.jsonl'
    },
    "logiqa": {
        "test": HEAD + 'logiqa-en.jsonl'
    },
    "aqua_rat": {
        "test": HEAD + 'aqua-rat.jsonl'
    },
    'math_agieval': {
        "test": HEAD + 'math.jsonl'
    },
    'few_shot': {
        'few_shot': 'https://raw.githubusercontent.com/microsoft/AGIEval/main/data/few_shot_prompts.csv'
    }

}


class AgiEval(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("2.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name=name, version=version, description=name)
        for name, version in zip(list(_URLS.keys()), [VERSION] * len(list(_URLS.keys())))
    ]

    # BUILDER_CONFIGS = [
    #     datasets.BuilderConfig(
    #         name="aqua_rat",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="sat_en",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="sat_en_wop",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="sat_math",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="lsat_ar",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="lsat_lr",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="lsat_rc",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="logiqa",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    #     datasets.BuilderConfig(
    #         name="math_agieval",
    #         version=VERSION,
    #         description=_DESCRIPTION,
    #     ),
    # ]
    DEFAULT_CONFIG_NAME = "aqua_rat"

    def _info(self):

        if self.config.name == "aqua_rat":
            features = datasets.Features(
                {
                    "question": datasets.Value("string"),
                    "options": datasets.features.Sequence(datasets.Value("string")),
                    "label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]),
                    "solution": datasets.Value("string"),
                }
            )
        elif self.config.name in ("sat_en", "sat_math"):
            features = datasets.Features(
                {"passage": datasets.Value("string"),
                 "question": datasets.Value("string"),
                 "options": datasets.features.Sequence(datasets.Value("string")),
                 "label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
                 "solution": datasets.Value("string"),
                 }
            )
        elif self.config.name == "sat_en_wop":
            features = datasets.Features(
                {"question": datasets.Value("string"),
                 "options": datasets.features.Sequence(datasets.Value("string")),
                 "label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
                 "solution": datasets.Value("string"),
                 }
            )
        elif self.config.name == "logiqa":
            features = datasets.Features(
                {"passage": datasets.Value("string"),
                 "question": datasets.Value("string"),
                 "options": datasets.features.Sequence(datasets.Value("string")),
                 "label": datasets.ClassLabel(num_classes=4, names=["A", "B", "C", "D"]),
                 "solution": datasets.Value("string"),
                 }
            )
        elif self.config.name == "math_agieval":
            features = datasets.Features(
                {"question": datasets.Value("string"),
                 "answer": datasets.Value("string"),
                 "solution": datasets.Value("string"),
                 "level": datasets.Value("int32"),
                 "type": datasets.Value("string"),
                 }
            )
        elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
            features = datasets.Features(
                {"passage": datasets.Value("string"),
                 "question": datasets.Value("string"),
                 "options": datasets.features.Sequence(datasets.Value("string")),
                 "label": datasets.ClassLabel(num_classes=5, names=["A", "B", "C", "D", "E"]),
                 "solution": datasets.Value("string"),
                 }
            )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        _urls = _URLS[self.config.name]
        urls = {
            "test": _urls["test"],
            "few_shot": _URLS["few_shot"]["few_shot"],
        }
        data_dir = dl_manager.download_and_extract(urls)
        splits = [datasets.SplitGenerator(
            name=datasets.Split.TEST,
            gen_kwargs={"filepath": data_dir["test"], "split": "test"},
        ), datasets.SplitGenerator(
            name="few_shot",
            gen_kwargs={"filepath": data_dir["few_shot"], "split": "few_shot"},
        )]

        return splits

    def _generate_examples(self, filepath, split):
        # Mapping for column names in CSV to dataset names
        names = {'aqua_rat': 'aqua-rat', 'sat_en': 'sat-en', 'sat_en_wop': 'sat-en','sat_math': 'sat-math',
                 'lsat_ar': 'lsat-ar', 'lsat_lr': 'lsat-lr', 'lsat_rc': 'lsat-rc',
                 'logiqa': 'logiqa-en', 'math_agieval': 'math'}

        if split == "few_shot":
            # Load the data from the CSV
            df = pd.read_csv(filepath, keep_default_na=False)

            # Extract samples and explanations
            samples = df[df.index % 2 == 0].reset_index(drop=True)
            explanations = df[df.index % 2 != 0].reset_index(drop=True)

            for key in range(samples.shape[0]):
                try:
                    data = ast.literal_eval(samples[names[self.config.name]][key])
                    explanation_row = explanations[names[self.config.name]][key]
                    if self.config.name == "aqua_rat":
                        yield key, {
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": str(explanation_row),
                        }
                    elif self.config.name == "logiqa":
                        yield key, {
                            "passage": data["passage"],
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": str(explanation_row),
                        }
                    elif self.config.name == "math_agieval":
                        if not data.get("level"):
                            data["level"] = data['other']['level']
                        if not data.get("type"):
                            data["type"] = data['other']['type']
                        yield key, {
                            "question": data["question"],
                            "answer": data["answer"],
                            "level": data["level"],
                            "type": data["type"],
                            "solution": str(explanation_row),
                        }
                    elif self.config.name in ("sat_en", "sat_math"):
                        yield key, {
                            "passage": data["passage"],
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": str(explanation_row),
                        }
                    elif self.config.name == "sat_en_wop":
                        yield key, {
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": str(explanation_row),
                        }
                    elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
                        yield key, {
                            "passage": data["passage"],
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": str(explanation_row),
                        }
                except:
                    pass
        else:
            with open(filepath, encoding="utf-8") as f:
                for key, row in enumerate(f):
                    data = json.loads(row)

                    if self.config.name == "aqua_rat":
                        yield key, {
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": data["other"]["solution"],
                        }
                    elif self.config.name == "logiqa":
                        yield key, {
                            "passage": data["passage"],
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": data["label"],
                        }
                    elif self.config.name == "math_agieval":
                        if not data.get("level"):
                            data["level"] = data['other']['level']
                        if not data.get("type"):
                            data["type"] = data['other']['type']
                        yield key, {
                            "question": data["question"],
                            "answer": data["answer"],
                            "solution": data["other"]["solution"],
                            "level": data["level"],
                            "type": data["type"],
                        }

                    elif self.config.name in ("sat_en", "sat_math"):
                        label_index = "ABCDE".index(data["label"])
                        if label_index > len(data["options"]) - 1:
                            continue
                        else:
                            yield key, {
                                "passage": data["passage"],
                                "question": data["question"],
                                "options": data["options"],
                                "label": data["label"],
                                "solution": data["other"]["solution"],
                            }
                    elif self.config.name == "sat_en_wop":
                        yield key, {
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": data["other"]["solution"],
                        }
                    elif self.config.name in ['lsat_lr', 'lsat_rc', 'lsat_ar']:
                        yield key, {
                            "passage": data["passage"],
                            "question": data["question"],
                            "options": data["options"],
                            "label": data["label"],
                            "solution": data["label"],
                        }