Upload test_stanford.py

test_stanford.py

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+"""Test-Stanford dataset by Bansal et al.."""
+
+import datasets
+import pandas as pd
+
+_CITATION = """
+@misc{bansal2015deep,
+      title={Towards Deep Semantic Analysis Of Hashtags}, 
+      author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
+      year={2015},
+      eprint={1501.03210},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
+"""
+
+_DESCRIPTION = """
+Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
+"""
+_URLS = {
+    "test": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/Test-Stanford.txt"
+}
+
+class TestStanford(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "index": datasets.Value("int32"),
+                    "hashtag": datasets.Value("string"),
+                    "segmentation": datasets.Value("string"),
+                    "gold_position": datasets.Value("int32"),
+                    "rank": datasets.Sequence(
+                        {
+                            "position": datasets.Value("int32"),
+                            "candidate": datasets.Value("string")
+                        }
+                    )
+                }
+            ),
+            supervised_keys=None,
+            homepage="",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download(_URLS)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"] }),
+        ]
+
+    def _generate_examples(self, filepath):
+
+        names = ["id","hashtag","candidate", "label"]
+        df = pd.read_csv(filepath, sep="\t", skiprows=1, header=None, 
+            names=names)
+
+        for col in names[0:-1]:
+            df[col] = df[col].apply(lambda x: x.strip("'").strip())
+
+        records = df.to_dict('records')
+
+        output = []
+
+        current_hashtag = None
+        hashtag = None
+        candidates = []
+        ids = []
+        label = []
+
+
+        for row in records:
+            hashtag = row["hashtag"]
+            if current_hashtag != hashtag:
+                new_row = {
+                    "hashtag": current_hashtag,
+                    "candidate": candidates,
+                    "id": ids,
+                    "label": label
+                }
+                
+                if current_hashtag:
+                    output.append(new_row)
+                
+                current_hashtag = row['hashtag']
+                candidates = [row["candidate"]]
+                ids = int(row["id"])
+                label = [int(row["label"])]
+            else:
+                candidates.append(row["candidate"])
+                label.append(int(row["label"]))            
+
+        def get_gold_position(row):
+            try:
+                return row["label"].index(1)
+            except ValueError:
+                return None
+
+        def get_rank(row):
+            return [{
+                "position": idx + 1,
+                "candidate": item
+            } for idx, item in enumerate(row["candidate"])]
+
+        def get_segmentation(row):
+            try:
+                gold_idx = row["label"].index(1)
+                return row["candidate"][gold_idx]
+            except ValueError:
+                return None
+
+        for idx, row in enumerate(output):
+            yield idx, {
+                "index": int(row["id"]),
+                "hashtag": row["hashtag"],
+                "segmentation": get_segmentation(row),
+                "gold_position": get_gold_position(row),
+                "rank": get_rank(row)
+            }