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README.md

@@ -1,3 +1,30 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- pol
+- tabular_classification
+- binary_classification
+pretty_name: Pol
+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:
+- pol
 ---
+# Pol
+The [Pol dataset](https://www.openml.org/search?type=data&sort=runs&id=151&status=active) from the [OpenML repository](https://www.openml.org/).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**         |
+|-------------------|---------------------------|-------------------------|
+| pol       | Binary classification     | Has the pol cost gone up?|
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/pol", "pol")["train"]
+```

pol.py

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+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+
+DESCRIPTION = "Pol 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/pol/raw/main/pol.csv"
+}
+features_types_per_config = {
+    "pol": {
+        "f1": datasets.Value("int64"),
+		"f2": datasets.Value("int64"),
+		"f3": datasets.Value("int64"),
+		"f4": datasets.Value("int64"),
+		"f5": datasets.Value("int64"),
+		"f6": datasets.Value("int64"),
+		"f7": datasets.Value("int64"),
+		"f8": datasets.Value("int64"),
+		"f9": datasets.Value("int64"),
+		"f10": datasets.Value("int64"),
+		"f11": datasets.Value("int64"),
+		"f12": datasets.Value("int64"),
+		"f13": datasets.Value("int64"),
+		"f14": datasets.Value("int64"),
+		"f15": datasets.Value("int64"),
+		"f16": datasets.Value("int64"),
+		"f17": datasets.Value("int64"),
+		"f18": datasets.Value("int64"),
+		"f19": datasets.Value("int64"),
+		"f20": datasets.Value("int64"),
+		"f21": datasets.Value("int64"),
+		"f22": datasets.Value("int64"),
+		"f23": datasets.Value("int64"),
+		"f24": datasets.Value("int64"),
+		"f25": datasets.Value("int64"),
+		"f26": datasets.Value("int64"),
+		"f27": datasets.Value("int64"),
+		"f28": datasets.Value("int64"),
+		"f29": datasets.Value("int64"),
+		"f30": datasets.Value("int64"),
+		"f31": datasets.Value("int64"),
+		"f32": datasets.Value("int64"),
+		"f33": datasets.Value("int64"),
+		"f34": datasets.Value("int64"),
+		"f35": datasets.Value("int64"),
+		"f36": datasets.Value("int64"),
+		"f37": datasets.Value("int64"),
+		"f38": datasets.Value("int64"),
+		"f39": datasets.Value("int64"),
+		"f40": datasets.Value("int64"),
+		"f41": datasets.Value("int64"),
+		"f42": datasets.Value("int64"),
+		"f43": datasets.Value("int64"),
+		"f44": datasets.Value("int64"),
+		"f45": datasets.Value("int64"),
+		"f46": datasets.Value("int64"),
+		"f47": datasets.Value("int64"),
+		"f48": datasets.Value("int64"),
+        "class": 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 ElectricityConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(ElectricityConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Electricity(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "pol"
+    BUILDER_CONFIGS = [
+        ElectricityConfig(name="pol",
+                    description="Electricity 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