Upload binkley.py

binkley.py

@@ -0,0 +1,86 @@
+"""BT11"""
+
+import datasets
+import pandas as pd
+from collections import deque
+
+_CITATION = """
+@inproceedings{li2018helpful,
+  title={Helpful or Not? An investigation on the feasibility of identifier splitting via CNN-BiLSTM-CRF.},
+  author={Li, Jiechu and Du, Qingfeng and Shi, Kun and He, Yu and Wang, Xin and Xu, Jincheng},
+  booktitle={SEKE},
+  pages={175--174},
+  year={2018}
+}
+"""
+
+_DESCRIPTION = """
+In programming languages, identifiers are tokens (also called symbols) which name language entities.
+Some of the kinds of entities an identifier might denote include variables, types, labels, subroutines, and packages.
+
+Binkley is a dataset for identifier segmentation, 
+i.e. the task of adding spaces between the words on a identifier.
+"""
+_URL = "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/binkley.csv"
+
+class Binkley(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "index": datasets.Value("int32"),
+                    "identifier": datasets.Value("string"),
+                    "segmentation": datasets.Value("string")
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URL)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files}),
+        ]
+
+    def _generate_examples(self, filepath):
+
+        def get_segmentation(needle, haystack, sep="-"):
+            output = haystack
+            needle = needle.lower()
+            haystack = haystack.lower()
+            counter = 0
+            pos = deque()
+            iterator = iter(haystack)
+            for char in needle:
+                if char == sep:
+                    pos.appendleft(counter)
+                    continue
+                while True:
+                    try:
+                        next_char = next(iterator)
+                        counter += 1
+                        if next_char == char:
+                            break
+                    except StopIteration:
+                        break
+            while pos:
+                next_pos = pos.popleft()
+                output = output[:next_pos] + " " + output[next_pos:]
+            return output
+
+        df = pd.read_csv(filepath, header=None)[[0,1]]
+        df = df.dropna()
+        records = df.to_dict("records")
+
+        for idx, item in enumerate(records):
+            yield idx, {
+                "index": idx,
+                "identifier": item[0],
+                "segmentation": get_segmentation(item[1], item[0])
+            }