updated to datasets 4.*

README.md

@@ -1,21 +1,25 @@
 ---
-language:
-- en
+configs:
+- config_name: abalone
+  data_files:
+  - path: abalone/train.csv
+    split: train
+  default: true
+- config_name: binary
+  data_files:
+  - path: binary/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Abalone
+size_categories: 1M<n<10M
 tags:
-- abalone
-- tabular_regression
-- regression
+- tabular_classification
 - binary_classification
-pretty_name: Abalone
-size_categories:
-- 1K<n<10K
+- multiclass_classification
 task_categories:
-- tabular-regression
 - tabular-classification
-configs:
-- abalone
-- binary
-license: cc
 ---
 # Abalone
 The [Abalone dataset](https://archive-beta.ics.uci.edu/dataset/1/abalone) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

abalone.py

@@ -1,116 +0,0 @@
-"""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"),
-        "number_of_rings": datasets.Value("int8")
-    },
-    "binary": {
-        "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"),
-        "is_old": datasets.ClassLabel(num_classes=2)
-    }
-}
-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."),
-        AbaloneConfig(name="binary", description="Abalone 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, header=None)
-        data.columns = _BASE_FEATURE_NAMES
-
-        if self.config.name == "binary":
-            data = data.rename(columns={"number_of_rings": "is_old"})
-            data["is_old"] = data["is_old"].apply(lambda x: 1 if x > 9 else 0)
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row

abalone.data → abalone/train.csv

@@ -1,3 +1,4 @@
+sex,length,diameter,height,whole_weight,shucked_weight,viscera_weight,shell_weight,number_of_rings
 M,0.455,0.365,0.095,0.514,0.2245,0.101,0.15,15
 M,0.35,0.265,0.09,0.2255,0.0995,0.0485,0.07,7
 F,0.53,0.42,0.135,0.677,0.2565,0.1415,0.21,9
@@ -1255,7 +1256,7 @@ I,0.42,0.305,0.09,0.328,0.168,0.0615,0.082,6
 I,0.425,0.325,0.11,0.3335,0.173,0.045,0.1,7
 I,0.425,0.32,0.1,0.3055,0.126,0.06,0.106,7
 I,0.425,0.31,0.09,0.301,0.1385,0.065,0.08,7
-I,0.43,0.34,0,0.428,0.2065,0.086,0.115,8
+I,0.43,0.34,0.0,0.428,0.2065,0.086,0.115,8
 I,0.43,0.315,0.095,0.378,0.175,0.08,0.1045,8
 I,0.435,0.315,0.11,0.3685,0.1615,0.0715,0.12,7
 I,0.44,0.34,0.12,0.438,0.2115,0.083,0.12,9
@@ -1366,7 +1367,7 @@ M,0.61,0.48,0.165,1.244,0.6345,0.257,0.305,12
 M,0.61,0.475,0.17,1.0265,0.435,0.2335,0.3035,10
 I,0.61,0.465,0.15,0.9605,0.4495,0.1725,0.286,9
 M,0.61,0.48,0.17,1.137,0.4565,0.29,0.347,10
-M,0.61,0.46,0.16,1,0.494,0.197,0.275,10
+M,0.61,0.46,0.16,1.0,0.494,0.197,0.275,10
 F,0.615,0.475,0.155,1.004,0.4475,0.193,0.2895,10
 M,0.615,0.47,0.165,1.128,0.4465,0.2195,0.34,10
 M,0.615,0.5,0.17,1.054,0.4845,0.228,0.295,10
@@ -3994,7 +3995,7 @@ M,0.7,0.6,0.23,2.003,0.8105,0.4045,0.5755,10
 M,0.72,0.6,0.235,2.2385,0.984,0.411,0.621,12
 I,0.185,0.135,0.045,0.032,0.011,0.0065,0.01,4
 I,0.245,0.175,0.055,0.0785,0.04,0.018,0.02,5
-I,0.315,0.23,0,0.134,0.0575,0.0285,0.3505,6
+I,0.315,0.23,0.0,0.134,0.0575,0.0285,0.3505,6
 I,0.36,0.27,0.09,0.2075,0.098,0.039,0.062,6
 I,0.375,0.28,0.08,0.2235,0.115,0.043,0.055,6
 I,0.415,0.31,0.095,0.34,0.181,0.057,0.083,6