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README.md

@@ -1,3 +1,44 @@
 ---
-license: cc
+language:
+- en
+tags:
+- abalone
+- tabular_regression
+- regression
+pretty_name: Abalone
+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-regression
+configs:
+- abalone
 ---
+# Abalone
+The [Abalone dataset](https://archive.ics.uci.edu/ml/datasets/Abalone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+Predict the age of the given abalone.
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | Description                                                   |
+|-------------------|---------------------------|---------------------------------------------------------------|
+| Abalon            | Regression                | Predict the age of the abalone.                               |
+
+# Usage
+```
+from datasets import load_dataset
+from sklearn.tree import DecisionTreeClassifier
+
+dataset = load_dataset("mstz/abalone", "abalone")["train"]
+```
+
+# Features
+|**Feature**        |**Type**       |
+|-------------------|---------------|
+| sex               | `[string]`    |
+| length            | `[float64]`   |
+| diameter          | `[float64]`   |
+| height            | `[float64]`   |
+| whole_weight      | `[float64]`   |
+| shucked_weight    | `[float64]`   |
+| viscera_weight    | `[float64]`   |
+| shell_weight      | `[float64]`   |
+| number_of_rings   | `[int8]`      |

abalone.py

@@ -0,0 +1,101 @@
+"""Abalone."""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_ORIGINAL_FEATURE_NAMES = [
+    "Sex",
+    "Length",
+    "Diameter",
+    "Height",
+    "Whole_weight",
+    "Shucked_weight",
+    "Viscera_weight",
+    "Shell_weight",
+    "Ring",
+]
+_BASE_FEATURE_NAMES = [
+    "sex",
+    "length",
+    "diameter",
+    "height",
+    "whole_weight",
+    "shucked_weight",
+    "viscera_weight",
+    "shell_weight",
+    "number_of_rings",
+]
+
+DESCRIPTION = "Abalone dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Abalone"
+_URLS = ("https://huggingface.co/datasets/mstz/abalone/raw/abalone.data")
+_CITATION = """
+@misc{misc_abalone_1,
+  title        = {{Abalone}},
+  year         = {1995},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C55C7W}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/abalone/raw/main/abalone.data",
+}
+features_types_per_config = {
+    "abalone": {
+        "sex": datasets.Value("string"),
+        "length": datasets.Value("float64"),
+        "diameter": datasets.Value("float64"),
+        "Height": datasets.Value("float64"),
+        "whole_weight": datasets.Value("float64"),
+        "shucked_weight": datasets.Value("float64"),
+        "viscera_weight": datasets.Value("float64"),
+        "shell_weight": datasets.Value("float64"),
+        "ring": datasets.Value("int8")
+    }
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class AbaloneConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(AbaloneConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Abalone(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "abalone"
+    BUILDER_CONFIGS = [
+        AbaloneConfig(name="abalone",
+                    description="Abalone for regression."),
+    ]
+
+
+    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, header=None)
+        data.columns = _BASE_FEATURE_NAMES
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row