Upload openea-d-w-100k-v1.py

openea-d-w-100k-v1.py

@@ -0,0 +1,152 @@
+import datasets
+import os
+import pickle
+import json
+
+class OpenEADW100kV1Config(datasets.BuilderConfig):
+
+    def __init__(self, features, data_url, citation, url, label_classes=("False", "True"), **kwargs):
+        super(OpenEADW100kV1Config, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
+
+
+class OpenEADW100kV1(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        OpenEADW100kV1Config(
+            name="source",
+            features=["column1", "column2", "column3"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/openea-d-w-100k-v1/resolve/main/openea-d-w-100k-v1.zip"
+        ),
+        OpenEADW100kV1Config(
+            name="target",
+            features=["column1", "column2", "column3"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/openea-d-w-100k-v1/resolve/main/openea-d-w-100k-v1.zip"
+        ),
+        OpenEADW100kV1Config(
+            name="pairs",
+            features=["left_id", "right_id"],
+            citation="TODO",
+            url="TODO",
+            data_url="https://huggingface.co/datasets/matchbench/openea-d-w-100k-v1/resolve/main/openea-d-w-100k-v1.zip"
+        ),
+    ]
+
+    def _info(self):
+        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("string") 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 ""
+        #task_name = _get_task_name_from_data_url(self.config.data_url)
+        #dl_dir = os.path.join(dl_dir, task_name)
+        if self.config.name == "source":
+            return [
+            datasets.SplitGenerator(
+                name="attr_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "attr_triples_1"),
+                    "split": "attr_triples",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "rel_triples_1"),
+                    "split": "rel_triples",
+                },
+            ),
+            ]
+        elif self.config.name == "target":
+            return [
+            datasets.SplitGenerator(
+                name="attr_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "attr_triples_2"),
+                    "split": "attr_triples",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="rel_triples",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "rel_triples_2"),
+                    "split": "rel_triples",
+                },
+            ),
+            ]
+        elif self.config.name == "pairs":
+            return [
+            datasets.SplitGenerator(
+                name="train",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "train_links"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="valid",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "valid_links"),
+                    "split": "valid",
+                },
+            ),
+            datasets.SplitGenerator(
+                name="test",
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, "test_links"),
+                    "split": "test",
+                },
+            )
+            ]
+
+
+    def _generate_examples(self, data_file, split):
+        f = open(data_file,"r",encoding='utf-8')
+        data = f.readlines()
+        for i in range(len(data)):
+            #print(row)
+            if self.config.name in ["source", "target"]:
+                if split in ["ent_ids","rel_ids"]:
+                    row = data[i].strip('\n').split('\t')
+                    yield i, {
+                                "column1": row[0],
+                                "column2": row[1],
+                                "column3": None
+                            }
+                elif split in ["rel_triples","rel_triples_whole"]:
+                    row = data[i].strip('\n').split('\t')
+                    yield i, {
+                                "column1": row[0],
+                                "column2": row[1],
+                                "column3": row[2]
+                            }
+                elif split in ["attr_triples"]:
+                    row = data[i].rstrip('\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]
+                }