Create Dataset loading script.py

Dataset loading script.py

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+import csv
+import os
+import datasets
+
+_DESCRIPTION = """
+This is a dataset for Marvel universe social network, which contains the relationships between Marvel heroes.
+"""
+_CITATION = """\
+@article{alberich2002marvel,
+  title={Marvel Universe looks almost like a real social network},
+  author={Alberich, Ricardo and Miro-Julia, Joe and Rossell{\'o}, Francesc},
+  journal={arXiv preprint cond-mat/0202174},
+  year={2002}
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/ShimizuYuki/Marvel_network"
+
+_LICENSE = "afl-3.0"
+
+_URLS = {
+    "adjacency_list": "https://drive.google.com/file/d/1wcINfLn25tMIVJcp6MtxSNR7QNF8GI_D/view?usp=sharing",
+    "hero_hero_comic": "https://drive.google.com/file/d/1wel0zjoa8GvBo255dlX7cVOPF9XbvQrI/view?usp=sharing",
+}
+
+class NCEducationDataset(datasets.GeneratorBasedBuilder):
+
+    VERSION = datasets.Version("1.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="adjacency_list", version=VERSION, description="This is a adjacency list for this network"),
+        datasets.BuilderConfig(name="hero_hero_comic", version=VERSION, description="This adds comic imformation to adjacency list"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "adjacency_list"
+
+    def _info(self):
+      if self.config.name == "adjacency_list":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+          features = datasets.Features(
+              {
+                  "hero1": datasets.Value("string"),
+                  "hero2": datasets.Value("string"),
+                  "counts": datasets.Value("int64")
+                  # These are the features of your dataset like images, labels ...
+              }
+          )
+      else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+          features = datasets.Features(
+              {
+                  "hero1": datasets.Value("string"),
+                  "hero2": datasets.Value("string"),
+                  "comic": datasets.Value("int64")
+                  # These are the features of your dataset like images, labels ...
+              }
+          )
+      return datasets.DatasetInfo(
+          description=_DESCRIPTION,
+          features=features,
+          homepage=_HOMEPAGE,
+          license=_LICENSE,
+          citation=_CITATION,
+      )
+
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_file = dl_manager.download(urls)
+        return [
+            datasets.SplitGenerator(
+                name = "train",
+                gen_kwargs = {
+                    "filepath": data_file,
+                },
+            )
+        ]
+
+    def _generate_examples(self, filepath):
+        data = {}
+        with open(filepath, "r", encoding="utf-8") as file:
+          csv_reader = csv.DictReader(file)
+          for row in csv_reader:
+            if self.config.name == "adjacency_list":
+              hero1 = row["hero1"]
+              hero2 = row["hero2"]
+              counts = int(row["counts"])
+            else:
+              hero1 = row["hero1"]
+              hero2 = row["hero2"]
+              comic = row["comic"]
+
+        for idx, (area_name, area_data) in enumerate(data.items()):
+          if self.config.name == "adjacency_list":
+            yield idx, {
+                "hero1": hero1,
+                "hero2": hero2,
+                "counts": counts,
+            }
+          else:
+            yield idx, {
+                "hero1": hero1,
+                "hero2": hero2,
+                "comic": comic,
+            }