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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.
import json
import datasets
_CITATION = """\
@misc{valmeekam2023planbenchextensiblebenchmarkevaluating,
title={PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change},
author={Karthik Valmeekam and Matthew Marquez and Alberto Olmo and Sarath Sreedharan and Subbarao Kambhampati},
year={2023},
eprint={2206.10498},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2206.10498},
}
"""
_DESCRIPTION = """\
PlanBench is a benchmark for evaluating models' capabilities of planning and reasoning by evaluating them on IPC problems"""
_HOMEPAGE = "https://github.com/karthikv792/LLMs-Planning/tree/main/plan-bench"
_LICENSE = "MIT"
_URLS_prefix = {
"blocksworld" : "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/blocksworld",
"blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/blocksworld_3",
"mystery_blocksworld": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/mystery_blocksworld",
"mystery_blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/mystery_blocksworld_3",
"logistics": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/main/plan-bench/prompts/logistics",
}
_URLS = {
"blocksworld_plan_generation": {
"test": _URLS_prefix["blocksworld"] + "/task_1_plan_generation.json"
},
"blocksworld_plan_optimality": {
"test": _URLS_prefix["blocksworld"] + "/task_2_plan_optimality.json"
},
"blocksworld_plan_verification": {
"test": _URLS_prefix["blocksworld"] + "/task_3_plan_verification.json"
},
"blocksworld_plan_reuse": {
"test": _URLS_prefix["blocksworld"] + "/task_4_plan_reuse.json"
},
"blocksworld_plan_generalization": {
"test": _URLS_prefix["blocksworld"] + "/task_5_plan_generalization.json"
},
"blocksworld_replanning": {
"test": _URLS_prefix["blocksworld"] + "/task_6_replanning.json"
},
"blocksworld_plan_execution": {
"test": _URLS_prefix["blocksworld"] + "/task_7_plan_execution.json"
},
"blocksworld_goal_shuffling": {
"test": _URLS_prefix["blocksworld"] + "/task_8_1_goal_shuffling.json"
},
"blocksworld_full_to_partial": {
"test": _URLS_prefix["blocksworld"] + "/task_8_2_full_to_partial.json"
},
"blocksworld_partial_to_full": {
"test": _URLS_prefix["blocksworld"] + "/task_8_3_partial_to_full.json"
},
"blocksworld_3_plan_generation": {
"test": _URLS_prefix["blocksworld_3"] + "/task_1_plan_generation.json"
},
"blocksworld_3_plan_optimality": {
"test": _URLS_prefix["blocksworld_3"] + "/task_2_plan_optimality.json"
},
"blocksworld_3_plan_verification": {
"test": _URLS_prefix["blocksworld_3"] + "/task_3_plan_verification.json"
},
"blocksworld_3_plan_reuse": {
"test": _URLS_prefix["blocksworld_3"] + "/task_4_plan_reuse.json"
},
"blocksworld_3_plan_generalization": {
"test": _URLS_prefix["blocksworld_3"] + "/task_5_plan_generalization.json"
},
"blocksworld_3_replanning": {
"test": _URLS_prefix["blocksworld_3"] + "/task_6_replanning.json"
},
"blocksworld_3_plan_execution": {
"test": _URLS_prefix["blocksworld_3"] + "/task_7_plan_execution.json"
},
"blocksworld_3_goal_shuffling": {
"test": _URLS_prefix["blocksworld_3"] + "/task_8_1_goal_shuffling.json"
},
"blocksworld_3_full_to_partial": {
"test": _URLS_prefix["blocksworld_3"] + "/task_8_2_full_to_partial.json"
},
"blocksworld_3_partial_to_full": {
"test": _URLS_prefix["blocksworld_3"] + "/task_8_3_partial_to_full.json"
},
"mystery_blocksworld_plan_generation": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_1_plan_generation.json"
},
"mystery_blocksworld_plan_optimality": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_2_plan_optimality.json"
},
"mystery_blocksworld_plan_verification": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_3_plan_verification.json"
},
"mystery_blocksworld_plan_reuse": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_4_plan_reuse.json"
},
"mystery_blocksworld_plan_generalization": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_5_plan_generalization.json"
},
"mystery_blocksworld_replanning": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_6_replanning.json"
},
"mystery_blocksworld_plan_execution": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_7_plan_execution.json"
},
"mystery_blocksworld_goal_shuffling": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_8_1_goal_shuffling.json"
},
"mystery_blocksworld_full_to_partial": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_8_2_full_to_partial.json"
},
"mystery_blocksworld_partial_to_full": {
"test": _URLS_prefix["mystery_blocksworld"] + "/task_8_3_partial_to_full.json"
},
"mystery_blocksworld_3_plan_generation": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_1_plan_generation.json"
},
"mystery_blocksworld_3_plan_optimality": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_2_plan_optimality.json"
},
"mystery_blocksworld_3_plan_verification": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_3_plan_verification.json"
},
"mystery_blocksworld_3_plan_reuse": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_4_plan_reuse.json"
},
"mystery_blocksworld_3_plan_generalization": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_5_plan_generalization.json"
},
"mystery_blocksworld_3_replanning": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_6_replanning.json"
},
"mystery_blocksworld_3_plan_execution": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_7_plan_execution.json"
},
"mystery_blocksworld_3_goal_shuffling": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_1_goal_shuffling.json"
},
"mystery_blocksworld_3_full_to_partial": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_2_full_to_partial.json"
},
"mystery_blocksworld_3_partial_to_full": {
"test": _URLS_prefix["mystery_blocksworld_3"] + "/task_8_3_partial_to_full.json"
},
"logistics_plan_generation": {
"test": _URLS_prefix["logistics"] + "/task_1_plan_generation.json"
},
"logistics_plan_optimality": {
"test": _URLS_prefix["logistics"] + "/task_2_plan_optimality.json"
},
"logistics_plan_verification": {
"test": _URLS_prefix["logistics"] + "/task_3_plan_verification.json"
},
"logistics_plan_reuse": {
"test": _URLS_prefix["logistics"] + "/task_4_plan_reuse.json"
},
"logistics_plan_generalization": {
"test": _URLS_prefix["logistics"] + "/task_5_plan_generalization.json"
},
"logistics_replanning": {
"test": _URLS_prefix["logistics"] + "/task_6_replanning.json"
},
"logistics_plan_execution": {
"test": _URLS_prefix["logistics"] + "/task_7_plan_execution.json"
},
"logistics_goal_shuffling": {
"test": _URLS_prefix["logistics"] + "/task_8_1_goal_shuffling.json"
},
"logistics_full_to_partial": {
"test": _URLS_prefix["logistics"] + "/task_8_2_full_to_partial.json"
},
"logistics_partial_to_full": {
"test": _URLS_prefix["logistics"] + "/task_8_3_partial_to_full.json"
}
}
class PlanBench(datasets.GeneratorBasedBuilder):
""" LMentry is a benchmark for measuring language model performance on tasks that are trivial to humans. LMentry consists of 25 tasks which humans are generally expected to perform perfectly, e.g. writing a sentence containing a specific word, identifying which words in a list belong to a specific category, choosing which of two words is longer, or identifying which of two words rhymes with a third word.
"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=config_name,
version=datasets.Version("0.0.1"),
description=f"{config_name} task from PlanBench"
)
for config_name in _URLS.keys()
]
def _info(self):
features = {
"instance_id": datasets.Value("int32"),
"query": datasets.Value("string"),
"ground_truth_plan": datasets.Value("string"),
}
if ("plan_generation" in self.config.name or
"plan_optimality" in self.config.name or
"plan_generalization" in self.config.name or
"replanning" in self.config.name or
"plan_execution" in self.config.name):
features.update({"example_instance_ids": datasets.Sequence(datasets.Value("string"))})
if "plan_reuse" in self.config.name or "replanning" in self.config.name:
features.update({"new_instance": datasets.Value("string")})
if "goal_shuffling" in self.config.name:
features.update({"single_goal_instances": datasets.Value("int32")})
features = datasets.Features(features)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
)
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.TEST,
gen_kwargs = {
"filepath" : data_dir["test"],
"split" : "test",
}
)
]
def _generate_examples(self, filepath, split):
with open(filepath, encoding = "utf-8") as fin :
data = json.load(fin)
for instance in data["instances"]:
yield instance["instance_id"], instance
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