Convert dataset to Parquet (#5)

- Convert dataset to Parquet (43d16a89f1798a9c9d84b5d23174b9e1984a3009)
- Add small data files (9c500d97d229a8475a1360bdb30532f16e99c9c1)
- Delete loading script (9a4575a0d7d8372d9374b8b0934cc1d3df3055a7)
- Delete loading script auxiliary file (9261f83b1c22a53e14300e01a2f91dfe5106830e)
- Delete loading script auxiliary file (62d56c3f7a50505fa750c299e8010b7f1bd423c5)

README.md

@@ -30,16 +30,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 32614834
+    num_bytes: 32614786
     num_examples: 52364
   - name: validation
-    num_bytes: 4086741
+    num_bytes: 4086733
     num_examples: 6546
   - name: test
-    num_bytes: 4063673
+    num_bytes: 4063665
     num_examples: 6545
-  download_size: 39979724
-  dataset_size: 40765248
+  download_size: 14929559
+  dataset_size: 40765184
 - config_name: small
   features:
   - name: id
@@ -50,16 +50,33 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 13006719
+    num_bytes: 13006679
     num_examples: 46680
   - name: validation
-    num_bytes: 1629250
+    num_bytes: 1629242
     num_examples: 5835
   - name: test
-    num_bytes: 1619708
+    num_bytes: 1619700
     num_examples: 5835
-  download_size: 15555421
-  dataset_size: 16255677
+  download_size: 5894462
+  dataset_size: 16255621
+configs:
+- config_name: medium
+  data_files:
+  - split: train
+    path: medium/train-*
+  - split: validation
+    path: medium/validation-*
+  - split: test
+    path: medium/test-*
+- config_name: small
+  data_files:
+  - split: train
+    path: small/train-*
+  - split: validation
+    path: small/validation-*
+  - split: test
+    path: small/test-*
 ---
 
 # Dataset Card for "code_x_glue_cc_code_refinement"

code_x_glue_cc_code_refinement.py

@@ -1,93 +0,0 @@
-from typing import List
-
-import datasets
-
-from .common import TrainValidTestChild
-from .generated_definitions import DEFINITIONS
-
-
-_DESCRIPTION = """We use the dataset released by this paper(https://arxiv.org/pdf/1812.08693.pdf). The source side is a Java function with bugs and the target side is the refined one. All the function and variable names are normalized. Their dataset contains two subsets ( i.e.small and medium) based on the function length."""
-_CITATION = """@article{10.1145/3340544,
-author = {Tufano, Michele and Watson, Cody and Bavota, Gabriele and Penta, Massimiliano Di and White, Martin and Poshyvanyk, Denys},
-title = {An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation},
-year = {2019},
-issue_date = {October 2019},
-publisher = {Association for Computing Machinery},
-address = {New York, NY, USA},
-volume = {28},
-number = {4},
-issn = {1049-331X},
-url = {https://doi-org.proxy.wm.edu/10.1145/3340544},
-doi = {10.1145/3340544},
-abstract = {Millions of open source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation, we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9--50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.},
-journal = {ACM Trans. Softw. Eng. Methodol.},
-month = sep,
-articleno = {19},
-numpages = {29},
-keywords = {bug-fixes, Neural machine translation}
-}"""
-
-
-class CodeXGlueCcCodeRefinementImpl(TrainValidTestChild):
-    _DESCRIPTION = _DESCRIPTION
-    _CITATION = _CITATION
-
-    _FEATURES = {
-        "id": datasets.Value("int32"),  # Index of the sample
-        "buggy": datasets.Value("string"),  # The buggy version of the code
-        "fixed": datasets.Value("string"),  # The correct version of the code
-    }
-
-    _SUPERVISED_KEYS = ["fixed"]
-
-    def generate_urls(self, split_name):
-        size = self.info["parameters"]["size"]
-        for key in "buggy", "fixed":
-            yield key, f"{size}/{split_name}.buggy-fixed.{key}"
-
-    def _generate_examples(self, split_name, file_paths):
-        """This function returns the examples in the raw (text) form."""
-        # Open each file (one for java, and one for c#)
-        files = {k: open(file_paths[k], encoding="utf-8") for k in file_paths}
-
-        id_ = 0
-        while True:
-            # Read a single line from each file
-            entries = {k: files[k].readline() for k in file_paths}
-
-            empty = self.check_empty(entries)
-            if empty:
-                # We are done: end of files
-                return
-
-            entries["id"] = id_
-            yield id_, entries
-            id_ += 1
-
-
-CLASS_MAPPING = {
-    "CodeXGlueCcCodeRefinement": CodeXGlueCcCodeRefinementImpl,
-}
-
-
-class CodeXGlueCcCodeRefinement(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(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,
-    }

generated_definitions.py

@@ -1,24 +0,0 @@
-DEFINITIONS = {
-    "medium": {
-        "class_name": "CodeXGlueCcCodeRefinement",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement",
-        "dir_name": "code-refinement",
-        "name": "medium",
-        "parameters": {"size": "medium"},
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-refinement",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data",
-        "sizes": {"test": 6545, "train": 52364, "validation": 6546},
-    },
-    "small": {
-        "class_name": "CodeXGlueCcCodeRefinement",
-        "dataset_type": "Code-Code",
-        "description": "CodeXGLUE code-refinement dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/code-refinement",
-        "dir_name": "code-refinement",
-        "name": "small",
-        "parameters": {"size": "small"},
-        "project_url": "https://github.com/madlag/CodeXGLUE/tree/main/Code-Code/code-refinement",
-        "raw_url": "https://raw.githubusercontent.com/madlag/CodeXGLUE/main/Code-Code/code-refinement/data",
-        "sizes": {"test": 5835, "train": 46680, "validation": 5835},
-    },
-}

medium/test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
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+oid sha256:776766b34878a999193dae39f0462ed1dc5aec3b7d219d3488db4c0127eca858
+size 1488083

medium/train-00000-of-00001.parquet

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+oid sha256:955496550f8603dd920aa2a09d1e4a14d878bda9f35f2f3575bae68b9d9493f0
+size 11943277

medium/validation-00000-of-00001.parquet

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+size 1498199

small/test-00000-of-00001.parquet

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+size 588578

small/train-00000-of-00001.parquet

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+oid sha256:6e0ddaf02afd36f2827d0f6edcf94b3b6b5908206cbf63fc6ac99ee4a6332472
+size 4715251

small/validation-00000-of-00001.parquet

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+oid sha256:b623a6544959b1767eb3082064b3b3896e26f0770dfd2270d14d4e62da0246a5
+size 590633