Upload planbench.py

planbench.py

@@ -0,0 +1,298 @@
+
+Hugging Face's logo Hugging Face
+
+Models
+Datasets
+Spaces
+Posts
+Docs
+Enterprise
+Pricing
+
+Datasets:
+clembench-playpen
+/
+lmentry
+Tasks:
+Question Answering
+Languages:
+English
+ArXiv:
+Dataset card
+Files and versions
+Community
+Settings
+lmentry
+/ lmentry.py
+momentino's picture
+momentino
+fix: name of a task
+268ba84
+verified
+8 days ago
+raw
+history
+blame
+edit
+delete
+13.6 kB
+# 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/tree/main/plan-bench/prompts/blocksworld",
+    "blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/tree/main/plan-bench/prompts/blocksworld_3",
+    "mystery_blocksworld": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/tree/main/plan-bench/prompts/mystery_blocksworld",
+    "mystery_blocksworld_3": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/tree/main/plan-bench/prompts/mystery_blocksworld_3",
+    "logistics": "https://raw.githubusercontent.com/karthikv792/LLMs-Planning/tree/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_reuse.json"
+    },
+    "blocksworld_replanning": {
+        "test": _URLS_prefix["blocksworld"] + "task_6_replanning.json"
+    },
+    "blocksworld_plan_execution": {
+        "test": _URLS_prefix["blocksworld"] + "task_7_plan_execution.json"
+    },
+    "blocksworld_plan_shuffling": {
+        "test": _URLS_prefix["blocksworld"] + "task_8_1_goal_shuffling.json"
+    },
+    "blocksworld_plan_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_reuse.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_plan_shuffling": {
+    "test": _URLS_prefix["blocksworld_3"] + "task_8_1_goal_shuffling.json"
+},
+"blocksworld_3_plan_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_reuse.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_plan_shuffling": {
+    "test": _URLS_prefix["mystery_blocksworld"] + "task_8_1_goal_shuffling.json"
+},
+"mystery_blocksworld_plan_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_reuse.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_plan_shuffling": {
+    "test": _URLS_prefix["mystery_blocksworld_3"] + "task_8_1_goal_shuffling.json"
+},
+"mystery_blocksworld_3_plan_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_reuse.json"
+},
+"logistics_replanning": {
+    "test": _URLS_prefix["logistics"] + "task_6_replanning.json"
+},
+"logistics_plan_execution": {
+    "test": _URLS_prefix["logistics"] + "task_7_plan_execution.json"
+},
+"logistics_plan_shuffling": {
+    "test": _URLS_prefix["logistics"] + "task_8_1_goal_shuffling.json"
+},
+"logistics_plan_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 or):
+            features.update({"example_istance_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