Convert dataset to Parquet (#3)

- Convert dataset to Parquet (a618859d5c7a7aed0c495d4b9578fd64b77475b4)
- Delete loading script (e5bc9f04f53ed043139d789b7a7490a503bb7763)
- Delete loading script auxiliary file (99c7ac386ba24b7411832d80e9eb9c5897ae9a9a)
- Delete loading script auxiliary file (6f91e2cd21a741b56f5f80fed63079c98c27f1f5)

README.md

@@ -32,16 +32,25 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 45723487
+    num_bytes: 45723451
     num_examples: 21854
   - name: validation
-    num_bytes: 5582545
+    num_bytes: 5582533
     num_examples: 2732
   - name: test
-    num_bytes: 5646752
+    num_bytes: 5646740
     num_examples: 2732
-  download_size: 61685715
-  dataset_size: 56952784
+  download_size: 22289955
+  dataset_size: 56952724
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
+  - split: test
+    path: data/test-*
 ---
 # Dataset Card for "code_x_glue_cc_defect_detection"
 

code_x_glue_cc_defect_detection.py

@@ -1,78 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
-The dataset we use comes from the paper Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks. We combine all projects and split 80%/10%/10% for training/dev/test."""
-_CITATION = """@inproceedings{zhou2019devign,
-title={Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks},
-author={Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang},
-booktitle={Advances in Neural Information Processing Systems},
-pages={10197--10207}, year={2019}"""
-
-
-class CodeXGlueCcDefectDetectionImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "func": datasets.Value("string"),  # The source code
-        "target": datasets.Value("bool"),  # 0 or 1 (vulnerability or not)
-        "project": datasets.Value("string"),  # Original project that contains this code
-        "commit_id": datasets.Value("string"),  # Commit identifier in the original project
-    }
-    _SUPERVISED_KEYS = ["target"]
-
-    def generate_urls(self, split_name):
-        yield "index", f"{split_name}.txt"
-        yield "data", "function.json"
-
-    def _generate_examples(self, split_name, file_paths):
-        import json
-
-        js_all = json.load(open(file_paths["data"], encoding="utf-8"))
-
-        index = set()
-        with open(file_paths["index"], encoding="utf-8") as f:
-            for line in f:
-                line = line.strip()
-                index.add(int(line))
-
-        for idx, js in enumerate(js_all):
-            if idx in index:
-                js["id"] = idx
-                js["target"] = int(js["target"]) == 1
-                yield idx, js
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcDefectDetection": CodeXGlueCcDefectDetectionImpl,
-}
-
-
-class CodeXGlueCcDefectDetection(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIG_CLASS = datasets.BuilderConfig
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name=name, description=info["description"]) for name, info in DEFINITIONS.items()
-    ]
-
-    def _info(self):
-        name = self.config.name
-        info = DEFINITIONS[name]
-        if info["class_name"] in CLASS_MAPPING:
-            self.child = CLASS_MAPPING[info["class_name"]](info)
-        else:
-            raise RuntimeError(f"Unknown python class for dataset configuration {name}")
-        ret = self.child._info()
-        return ret
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        return self.child._split_generators(dl_manager=dl_manager)
-
-    def _generate_examples(self, split_name, file_paths):
-        return self.child._generate_examples(split_name, file_paths)

common.py

@@ -1,75 +0,0 @@
-from typing import List
-
-import datasets
-
-
-# Citation, taken from https://github.com/microsoft/CodeXGLUE
-_DEFAULT_CITATION = """@article{CodeXGLUE,
-         title={CodeXGLUE: A Benchmark Dataset and Open Challenge for Code Intelligence},
-         year={2020},}"""
-
-
-class Child:
-    _DESCRIPTION = None
-    _FEATURES = None
-    _CITATION = None
-    SPLITS = {"train": datasets.Split.TRAIN}
-    _SUPERVISED_KEYS = None
-
-    def __init__(self, info):
-        self.info = info
-
-    def homepage(self):
-        return self.info["project_url"]
-
-    def _info(self):
-        # This is the description that will appear on the datasets page.
-        return datasets.DatasetInfo(
-            description=self.info["description"] + "\n\n" + self._DESCRIPTION,
-            features=datasets.Features(self._FEATURES),
-            homepage=self.homepage(),
-            citation=self._CITATION or _DEFAULT_CITATION,
-            supervised_keys=self._SUPERVISED_KEYS,
-        )
-
-    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
-        SPLITS = self.SPLITS
-        _URL = self.info["raw_url"]
-        urls_to_download = {}
-        for split in SPLITS:
-            if split not in urls_to_download:
-                urls_to_download[split] = {}
-
-            for key, url in self.generate_urls(split):
-                if not url.startswith("http"):
-                    url = _URL + "/" + url
-                urls_to_download[split][key] = url
-
-        downloaded_files = {}
-        for k, v in urls_to_download.items():
-            downloaded_files[k] = dl_manager.download_and_extract(v)
-
-        return [
-            datasets.SplitGenerator(
-                name=SPLITS[k],
-                gen_kwargs={"split_name": k, "file_paths": downloaded_files[k]},
-            )
-            for k in SPLITS
-        ]
-
-    def check_empty(self, entries):
-        all_empty = all([v == "" for v in entries.values()])
-        all_non_empty = all([v != "" for v in entries.values()])
-
-        if not all_non_empty and not all_empty:
-            raise RuntimeError("Parallel data files should have the same number of lines.")
-
-        return all_empty
-
-
-class TrainValidTestChild(Child):
-    SPLITS = {
-        "train": datasets.Split.TRAIN,
-        "valid": datasets.Split.VALIDATION,
-        "test": datasets.Split.TEST,
-    }

data/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:259c944eb4f8689e83895de476a00fe4cadf4839636769e725d3744fc36cfcd5
+size 2227970

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e319c83e2e816a10aeeebe78668aa95b757b04e555700bf5f826766b0e80bb06
+size 17847670

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a17a76ed040d7f8657d1ff741f967b44704c008101ac958e2990ad468203cfa4
+size 2214315

generated_definitions.py

@@ -1,12 +0,0 @@
-DEFINITIONS = {
-    "default": {
-        "class_name": "CodeXGlueCcDefectDetection",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE Defect-detection dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection",
-        "dir_name": "Defect-detection",
-        "name": "default",
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/Defect-detection",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/Defect-detection/dataset",
-        "sizes": {"test": 2732, "train": 21854, "validation": 2732},
-    }
-}