Upload Scotch.py

Scotch.py

@@ -0,0 +1,159 @@
+# coding=utf-8
+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+import pickle 
+from pathlib import Path
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """
+@inproceedings{
+dahal2022scotch,
+title={Scotch: A Semantic Code Search Engine for {IDE}s},
+author={Samip Dahal and Adyasha Maharana and Mohit Bansal},
+booktitle={Deep Learning for Code Workshop},
+year={2022},
+url={https://openreview.net/forum?id=rSxfCiOZk-c}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Scotch is a dataset of about 19 million functions collected from open-source repositiories from GitHub with permissive licenses. Each function has its corresponding code context and about 4 million functions have corresponding docstrings. The dataset includes functions written in programming languages Python, Java, Javascript, and Go."""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/sdpmas/Scotch"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "The MIT License"
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+languages=['python','javascript','java','go']
+_URLs = {lang:f'https://scotch.s3.amazonaws.com/{lang}.tar.gz' for lang in languages}
+_URLs['all']=_URLs.copy()
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class ScotchDataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="all", version=VERSION, description="All available data with docstrings"),
+        datasets.BuilderConfig(name="python", version=VERSION, description="Python data"),
+        datasets.BuilderConfig(name="javascript", version=VERSION, description="Javascript data"),
+        datasets.BuilderConfig(name="java", version=VERSION, description="Java data"),
+        datasets.BuilderConfig(name="go", version=VERSION, description="Go data"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "all"
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        
+        features = datasets.Features(
+            {
+                "repository_name": datasets.Value("string"),
+                "function_path": datasets.Value("string"),
+                "function_identifier": datasets.Value("string"),
+                "language": datasets.Value("string"),
+                "function": datasets.Value("string"),
+                "docstring": datasets.Value("string"),
+                "function_url": datasets.Value("string"),
+                "license":datasets.Value("string"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  # Here we define them above because they are different between the two configurations
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _URLs[self.config.name]
+        if isinstance(my_urls, str):
+            my_urls = {self.config.name:my_urls}
+        data_dir = [os.path.join(lang_dir,lang) for lang,lang_dir in dl_manager.download_and_extract(my_urls).items()]
+        
+        # splitpaths={split:[os.path.join(lang_dir,f'{split}.bin') for lang_dir in data_dir] for split in ['train','valid','test']}
+        splitpaths={}
+        for split in ['train','valid','test']:
+            for lang_dir in data_dir:
+                # Path glob .bin files
+                lang_split_files=sorted(Path(os.path.join(lang_dir,split)).glob('*.bin'))
+                if not split in splitpaths:
+                    splitpaths[split]=lang_split_files
+                else:
+                    splitpaths.extend(lang_split_files)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": splitpaths['train'],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": splitpaths['test'],
+                    "split": "test"
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": splitpaths['valid'],
+                    "split": "valid",
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, filepath,split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        count=-1
+        for i,filepath in enumerate(filepath):
+            loaded_f=pickle.load(open(filepath,'rb'))
+            for j, func in enumerate(loaded_f):
+                count+=1
+                yield count,{
+                    "repository_name": str(func['nwo']),
+                    "function_path":str(func['path']),
+                    "function_identifier": str(func['identifier']),
+                    "language": str(func['language']),
+                    "function": str(func['function']),
+                    "docstring": str(func['docstring']),
+                    "function_url": str(func['url']),
+                    "license":str(func['license']), 
+                }
+