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"""The Loyola University of Delaware Identifier Splitting Oracle"""
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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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@article{hill2014empirical,
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title={An empirical study of identifier splitting techniques},
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author={Hill, Emily and Binkley, David and Lawrie, Dawn and Pollock, Lori and Vijay-Shanker, K},
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journal={Empirical Software Engineering},
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volume={19},
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number={6},
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pages={1754--1780},
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year={2014},
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publisher={Springer}
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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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The Loyola University of Delaware Identifier Splitting Oracle 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/loyola-udelaware-identifier-splitting-oracle.txt"
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class Loyola(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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"language": datasets.Value("string"),
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"source": datasets.Value("string")
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}
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),
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supervised_keys=None,
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homepage="http://www.cs.loyola.edu/~binkley/ludiso/",
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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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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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output = haystack
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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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with open(filepath, 'r') as f:
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records = f.read().split("\n")
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records = [x for x in records if x]
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records = [x.split(" ") for x in 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[1],
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"segmentation": get_segmentation(item[4], item[1]),
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"language": item[2],
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"source": item[3],
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} |
