Upload ClassEval.py

ClassEval.py

@@ -0,0 +1,105 @@
+import json
+import datasets
+
+_DESCRIPTION = """\
+FudanSELab ClassEval
+"""
+_URL = "ClassEval_data.json"
+
+_CITATION = """\
+@misc{du2023classeval,
+      title={ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation}, 
+      author={Xueying Du and Mingwei Liu and Kaixin Wang and Hanlin Wang and Junwei Liu and Yixuan Chen and Jiayi Feng and Chaofeng Sha and Xin Peng and Yiling Lou},
+      year={2023},
+      eprint={2308.01861},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}"""
+
+_HOMEPAGE = "https://github.com/FudanSELab/ClassEval"
+
+_LICENSE = "MIT"
+
+class ClassEval(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="class_eval",
+            version=datasets.Version("1.0.0"),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    def _info(self):
+        method_feature = datasets.Features(
+            {
+                "method_name": datasets.Value("string"),
+                "method_description": datasets.Value("string"),
+                "test_class": datasets.Value("string"),
+                "test_code": datasets.Value("string"),
+                "solution_code": datasets.Value("string"),
+                "dependencies": {
+                    "Standalone": datasets.Value("bool"),
+                    "lib_dependencies": datasets.Sequence(datasets.Value("string")),
+                    "field_dependencies": datasets.Sequence(datasets.Value("string")),
+                    "method_dependencies": datasets.Sequence(datasets.Value("string")),
+                }
+            }
+        )
+
+        features = datasets.Features(
+            {
+                "task_id": datasets.Value("string"),
+                "skeleton": datasets.Value("string"),
+                "test": datasets.Value("string"),
+                "solution_code": datasets.Value("string"),
+                "import_statement": datasets.Sequence(datasets.Value("string")),
+                "class_description": datasets.Value("string"),
+                "methods_info": [method_feature],
+                "class_name": datasets.Value("string"),
+                "test_classes": datasets.Sequence(datasets.Value("string")),
+                "class_constructor": datasets.Value("string"),
+                "fields": datasets.Sequence(datasets.Value("string")),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        key = 0
+        with open(filepath, encoding = 'utf-8') as f:
+            cont = json.load(f)
+            for row in cont:
+                yield key, {
+                    "task_id": row["task_id"],
+                    "skeleton": row["skeleton"],
+                    "test": row["test"],
+                    "solution_code": row["solution_code"],
+                    "import_statement": row["import_statement"],
+                    "class_description": row["class_description"],
+                    "methods_info": row["methods_info"],
+                    "class_name": row["class_name"],
+                    "test_classes": row["test_classes"],
+                    "class_constructor": row["class_constructor"],
+                    "fields": row["fields"],
+                }
+                key += 1