Upload 3 files

README.md

@@ -1,3 +1,31 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- twonorm
+- tabular_classification
+- binary_classification
+pretty_name: Two Norm
+size_categories:
+- 1K<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- 8hr
+- 1hr
 ---
+# TwoNorm
+The [TwoNorm dataset](https://www.openml.org/search?type=data&status=active&id=1507) from the [OpenML repository](https://www.openml.org/).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  |
+|-------------------|---------------------------|
+| twonorm           | Binary classification     |
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/twonorm", "twonorm")["train"]
+```

twonorm.py

@@ -0,0 +1,64 @@
+"""TwoNorm"""
+
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+DESCRIPTION = "TwoNorm dataset from the OpenML repository."
+_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=1507"
+_URLS = ("https://www.openml.org/search?type=data&status=active&id=1507")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/two_norm/raw/main/twonorm.csv"
+}
+features_types_per_config = {
+    "twonorm": {
+	    datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+    },
+    
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class TwoNormConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(TwoNormConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class TwoNorm(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "twonorm"
+    BUILDER_CONFIGS = [
+        TwoNormConfig(name="twonorm",
+                      description="TwoNorm for binary classification.")
+        ]
+
+
+    def _info(self):       
+        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
+                                    features=features_per_config[self.config.name])
+
+        return info
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        downloads = dl_manager.download_and_extract(urls_per_split)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads[self.config.name]["train"]})
+        ]
+    
+    def _generate_examples(self, filepath: str):
+        data = pandas.read_csv(filepath)
+        
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row