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README.md

@@ -1,3 +1,30 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- house16
+- tabular_classification
+- binary_classification
+pretty_name: House16
+size_categories:
+- 10k<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- house16
 ---
+# House16
+The [House16 dataset](https://www.openml.org/search?type=data&sort=runs&id=821&status=active) from the [OpenML repository](https://www.openml.org/).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  |
+|-------------------|---------------------------|
+| house16           | Binary classification     |
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/house16", "house16")["train"]
+```

house16.py

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+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+
+DESCRIPTION = "House16 dataset from the OpenML repository."
+_HOMEPAGE = "https://www.openml.org/search?type=data&sort=runs&id=722&status=active"
+_URLS = ("https://www.openml.org/search?type=data&sort=runs&id=722&status=active")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/house16/raw/main/house_16H.csv"
+}
+features_types_per_config = {
+    "house16": {
+		"P1": datasets.Value("int64"),
+		"P5p1": datasets.Value("float64"),
+		"P6p2": datasets.Value("float64"),
+		"P11p4": datasets.Value("float64"),
+		"P14p9": datasets.Value("float64"),
+		"P15p1": datasets.Value("float64"),
+		"P15p3": datasets.Value("float64"),
+		"P16p2": datasets.Value("float64"),
+		"P18p2": datasets.Value("float64"),
+		"P27p4": datasets.Value("float64"),
+		"H2p2": datasets.Value("float64"),
+		"H8p2": datasets.Value("float64"),
+		"H10p1": datasets.Value("float64"),
+		"H13p1": datasets.Value("float64"),
+		"H18pA": datasets.Value("float64"),
+		"H40p4": datasets.Value("float64"),
+		"binaryClass": 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 House16Config(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(House16Config, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class House16(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "house16"
+    BUILDER_CONFIGS = [
+        House16Config(name="house16",
+                    description="House16 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["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