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Browse files- README.md +42 -1
- abalone.data +0 -0
- abalone.py +101 -0
README.md
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---
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---
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---
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language:
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- en
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tags:
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- abalone
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- tabular_regression
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- regression
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pretty_name: Abalone
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size_categories:
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- 1K<n<10K
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task_categories: # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
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- tabular-regression
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configs:
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- abalone
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---
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# Abalone
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The [Abalone dataset](https://archive.ics.uci.edu/ml/datasets/Abalone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
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Predict the age of the given abalone.
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# Configurations and tasks
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| **Configuration** | **Task** | Description |
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|-------------------|---------------------------|---------------------------------------------------------------|
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| Abalon | Regression | Predict the age of the abalone. |
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# Usage
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```
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from datasets import load_dataset
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from sklearn.tree import DecisionTreeClassifier
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dataset = load_dataset("mstz/abalone", "abalone")["train"]
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```
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# Features
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|**Feature** |**Type** |
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|-------------------|---------------|
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| sex | `[string]` |
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| length | `[float64]` |
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| diameter | `[float64]` |
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| height | `[float64]` |
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| whole_weight | `[float64]` |
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| shucked_weight | `[float64]` |
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| viscera_weight | `[float64]` |
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| shell_weight | `[float64]` |
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| number_of_rings | `[int8]` |
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abalone.data
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abalone.py
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"""Abalone."""
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from typing import List
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from functools import partial
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import datasets
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import pandas
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VERSION = datasets.Version("1.0.0")
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_ORIGINAL_FEATURE_NAMES = [
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"Sex",
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"Length",
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"Diameter",
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"Height",
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"Whole_weight",
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"Shucked_weight",
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"Viscera_weight",
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"Shell_weight",
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"Ring",
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]
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_BASE_FEATURE_NAMES = [
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"sex",
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"length",
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"diameter",
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"height",
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"whole_weight",
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"shucked_weight",
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"viscera_weight",
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"shell_weight",
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"number_of_rings",
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]
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DESCRIPTION = "Abalone dataset from the UCI ML repository."
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_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Abalone"
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_URLS = ("https://huggingface.co/datasets/mstz/abalone/raw/abalone.data")
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_CITATION = """
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@misc{misc_abalone_1,
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title = {{Abalone}},
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year = {1995},
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howpublished = {UCI Machine Learning Repository},
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note = {{DOI}: \\url{10.24432/C55C7W}}
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}"""
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# Dataset info
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urls_per_split = {
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"train": "https://huggingface.co/datasets/mstz/abalone/raw/main/abalone.data",
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}
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features_types_per_config = {
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"abalone": {
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"sex": datasets.Value("string"),
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"length": datasets.Value("float64"),
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"diameter": datasets.Value("float64"),
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"Height": datasets.Value("float64"),
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"whole_weight": datasets.Value("float64"),
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"shucked_weight": datasets.Value("float64"),
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"viscera_weight": datasets.Value("float64"),
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"shell_weight": datasets.Value("float64"),
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"ring": datasets.Value("int8")
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}
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}
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
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class AbaloneConfig(datasets.BuilderConfig):
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def __init__(self, **kwargs):
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super(AbaloneConfig, self).__init__(version=VERSION, **kwargs)
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self.features = features_per_config[kwargs["name"]]
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class Abalone(datasets.GeneratorBasedBuilder):
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# dataset versions
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DEFAULT_CONFIG = "abalone"
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BUILDER_CONFIGS = [
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AbaloneConfig(name="abalone",
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description="Abalone for regression."),
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]
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def _info(self):
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
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features=features_per_config[self.config.name])
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return info
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
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downloads = dl_manager.download_and_extract(urls_per_split)
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return [
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
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]
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def _generate_examples(self, filepath: str):
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data = pandas.read_csv(filepath, header=None)
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data.columns = _BASE_FEATURE_NAMES
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for row_id, row in data.iterrows():
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data_row = dict(row)
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yield row_id, data_row
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