Create selfkg-dwy100k-dbpwd.py

selfkg-dwy100k-dbpwd.py

@@ -0,0 +1,194 @@
+import json
+import os
+import pickle
+
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+# _SUBFIELD = "yg"
+
+# _VERSION = "1.0.0"
+
+
+# _CITATION = """\
+#     @inproceedings{sun2018bootstrapping,
+#     title={Bootstrapping Entity Alignment with Knowledge Graph Embedding.},
+#     author={Sun, Zequn and Hu, Wei and Zhang, Qingheng and Qu, Yuzhong},
+#     booktitle={IJCAI},
+#     volume={18},
+#     pages={4396--4402},
+#     year={2018}
+# }
+# """
+
+# _URL = "https://dl.acm.org/doi/10.1145/3485447.3511945"
+
+# _PREFIX = "https://huggingface.co/datasets/matchbench/selfkg-dwy100k-dbpwd"
+
+
+class SelfkgDwy100kwdConfig(datasets.BuilderConfig):
+    """BuilderConfig for Selfkg-DWY100k."""
+
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        """
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SelfkgDwy100kwdConfig, self).__init__(**kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+class DWY100kWd(datasets.GeneratorBasedBuilder):
+    """DWY100k-wd: A Entity Alignment Dataset."""
+
+    BUILDER_CONFIGS = [
+        SelfkgDwy100kwdConfig(
+            name="source",
+            features=["column1", "column2", "column3"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/selfkg-dwy100k-dbpwd/resolve/main/selfkg-dwy100k-dbpwd.zip"
+        ),
+        SelfkgDwy100kwdConfig(
+            name="target",
+            features=["column1", "column2", "column3"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/selfkg-dwy100k-dbpwd/resolve/main/selfkg-dwy100k-dbpwd.zip"
+        ),
+        SelfkgDwy100kwdConfig(
+            name="pairs",
+            features=["left_id","right_id"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/selfkg-dwy100k-dbpwd/resolve/main/selfkg-dwy100k-dbpwd.zip"
+        ),
+    ]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.name=="source":
+            features = {feature: datasets.Value("string") for feature in self.config.features}
+        elif self.config.name=="target":
+            features = {feature: datasets.Value("string") for feature in self.config.features}
+        elif self.config.name=="pairs":
+            features = {feature: datasets.Value("int32") for feature in self.config.features}
+        
+        return datasets.DatasetInfo(features = datasets.Features(features))
+
+
+    def _split_generators(self, dl_manager):
+
+        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
+        if self.config.name == "source":
+            return [
+            datasets.SplitGenerator(
+                name="ent_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "id_ent_1"),
+                    "split": "ent_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples_id",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "triples_1"),
+                    "split": "rel_triples_id",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="LaBSE_emb",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "raw_LaBSE_emb_1.pkl"),
+                    "split": "LaBSE_emb",
+                },
+            ),
+            ]
+        elif self.config.name == "target":
+            return [
+            datasets.SplitGenerator(
+                name="ent_ids",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "id_ent_2"),
+                    "split": "ent_ids",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples_id",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "triples_2"),
+                    "split": "rel_triples_id",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="LaBSE_emb",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "raw_LaBSE_emb_2.pkl"),
+                    "split": "LaBSE_emb",
+                },
+            ),
+            ]
+        elif self.config.name == "pairs":
+            return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "ref_ent_ids"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="valid",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "valid.ref"),
+                    "split": "valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "ref_ent_ids"),
+                    "split": "test",
+                },
+            ),
+            ]
+        
+    def _generate_examples(self, data_file, split):
+        if split in ["LaBSE_emb"]:
+            des = pickle.load(open(data_file,"rb"))
+            i = -1
+            for ent_ids,ori_emb in des.items():
+                i += 1
+                yield i, {
+                    "column1": ent_ids,
+                    "column2": ori_emb,
+                    "column3": None
+                }
+        else:    
+            f = open(data_file,"r", encoding='utf-8')
+            data = f.readlines()
+            for i in range(len(data)):
+                if self.config.name in ["source", "target"]:
+                    if split in ["ent_ids"]:
+                        row = data[i].strip('\n').split('\t')
+                        yield i, {
+                                    "column1": row[0],
+                                    "column2": row[1],
+                                    "column3": None
+                                }
+                    elif split in ["rel_triples_id"]:
+                        row = data[i].strip('\n').split('\t')
+                        yield i, {
+                                    "column1": row[0],
+                                    "column2": row[1],
+                                    "column3": row[2]
+                                }
+                if self.config.name == "pairs":
+                    row = data[i].strip('\n').split('\t')
+                    yield i, {
+                                "left_id": row[0],
+                                "right_id": row[1]
+                    }