Upload relHeter.py

relHeter.py

@@ -0,0 +1,98 @@
+import os
+import datasets
+import pandas as pd
+
+
+class relHeterConfig(datasets.BuilderConfig):
+    def __init__(self, features, data_url, **kwargs):
+        super(relHeterConfigConfig, self).__init__(**kwargs)
+        self.features = features
+        self.data_url = data_url
+
+
+class relHeterConfig(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        relHeterConfig(
+            name="pairs",
+            features={
+                "ltable_id": datasets.Value("string"),
+                "rtable_id": datasets.Value("string"),
+                "label": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-heter/resolve/main/",
+        ),
+        relHeterConfig(
+            name="source",
+            features={
+                "id": datasets.Value("string"),
+                "title": datasets.Value("string"),
+                "address": datasets.Value("string"),
+                "phone": datasets.Value("string"),
+                "category": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-heter/resolve/main/tableA.csv",
+        ),
+        relHeterConfig(
+            name="target",
+            features={
+                "id": datasets.Value("string"),
+                "add": datasets.Value("string"),
+                "city": datasets.Value("string"),
+                "phone": datasets.Value("string"),
+                "type": datasets.Value("string"),
+                "class": datasets.Value("string"),
+            },
+            data_url="https://huggingface.co/datasets/matchbench/rel-heter/resolve/main/tableB.csv",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            features=datasets.Features(self.config.features)
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name == "pairs":
+            return [
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "path_file": dl_manager.download_and_extract(
+                            os.path.join(self.config.data_url, f"{split}.csv")),
+                        "split": split,
+                    }
+                )
+                for split in ["train", "valid", "test"]
+            ]
+        if self.config.name == "source":
+            return [datasets.SplitGenerator(name="source", gen_kwargs={
+                "path_file": dl_manager.download_and_extract(self.config.data_url), "split": "source", })]
+        if self.config.name == "target":
+            return [datasets.SplitGenerator(name="target", gen_kwargs={
+                "path_file": dl_manager.download_and_extract(self.config.data_url), "split": "target", })]
+
+    def _generate_examples(self, path_file, split):
+        file = pd.read_csv(path_file)
+        for i, row in file.iterrows():
+            if split not in ['source', 'target']:
+                yield i, {
+                    "ltable_id": row["ltable_id"],
+                    "rtable_id": row["rtable_id"],
+                    "label": row["label"],
+                }
+            elif split in ['source']:
+                yield i, {
+                    "id": row["id"],
+                    "title": row["title"],
+                    "address": row["address"],
+                    "phone": row["phone"],
+                    "category": row["category"]},
+            else:
+                yield i, {
+                    "id": row["id"],
+                    "addr": row["addr"],
+                    "city": row["city"],
+                    "phone": row["phone"],
+                    "type": row["type"],
+                    "class": row["class"],
+                }