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"""Binkley"""
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import datasets
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import pandas as pd
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from collections import deque
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_CITATION = """
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@inproceedings{inproceedings,
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author = {Lawrie, Dawn and Binkley, David and Morrell, Christopher},
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year = {2010},
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month = {11},
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pages = {3 - 12},
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title = {Normalizing Source Code Vocabulary},
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journal = {Proceedings - Working Conference on Reverse Engineering, WCRE},
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doi = {10.1109/WCRE.2010.10}
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}
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"""
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_DESCRIPTION = """
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In programming languages, identifiers are tokens (also called symbols) which name language entities.
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Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
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Binkley is a dataset for identifier segmentation,
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i.e. the task of adding spaces between the words on a identifier.
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"""
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_URL = "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/binkley.csv"
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class Binkley(datasets.GeneratorBasedBuilder):
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VERSION = datasets.Version("1.0.0")
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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"index": datasets.Value("int32"),
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"identifier": datasets.Value("string"),
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"segmentation": datasets.Value("string")
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}
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),
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supervised_keys=None,
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homepage="",
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citation=_CITATION,
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)
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def _split_generators(self, dl_manager):
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downloaded_files = dl_manager.download(_URL)
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return [
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files}),
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]
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def _generate_examples(self, filepath):
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def get_segmentation(needle, haystack, sep="-"):
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output = haystack
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needle = needle.lower()
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haystack = haystack.lower()
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counter = 0
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pos = deque()
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iterator = iter(haystack)
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for char in needle:
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if char == sep:
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pos.appendleft(counter)
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continue
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while True:
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try:
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next_char = next(iterator)
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counter += 1
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if next_char == char:
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break
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except StopIteration:
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break
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while pos:
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next_pos = pos.popleft()
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output = output[:next_pos] + " " + output[next_pos:]
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return output
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df = pd.read_csv(filepath, header=None)[[0,1]]
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df = df.dropna()
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records = df.to_dict("records")
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for idx, item in enumerate(records):
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yield idx, {
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"index": idx,
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"identifier": item[0],
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"segmentation": get_segmentation(item[1], item[0])
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} |
