updated to datasets 4.*

README.md

@@ -1,19 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: survival
+  data_files:
+  - path: survival/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Haberman
+size_categories: 1M<n<10M
 tags:
-- haberman
 - tabular_classification
 - binary_classification
 - multiclass_classification
-pretty_name: Haberman
-size_categories:
-- n<1K
 task_categories:
 - tabular-classification
-configs:
-- survival
-license: cc
 ---
 # Haberman
 The [Haberman dataset](https://archive.ics.uci.edu/ml/datasets/Haberman) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

haberman.py

@@ -1,72 +0,0 @@
-"""Haberman"""
-
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-DESCRIPTION = "Haberman dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Haberman"
-_URLS = ("https://archive.ics.uci.edu/ml/datasets/Haberman")
-_CITATION = """
-@misc{misc_haberman's_survival_43,
-  author       = {Haberman,S.},
-  title        = {{Haberman's Survival}},
-  year         = {1999},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5XK51}}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/haberman/raw/main/haberman.data"
-}
-features_types_per_config = {
-    "survival": {
-        "age": datasets.Value("int32"),
-        "year_of_operation": datasets.Value("int32"),
-        "number_of_axillary_nodes": datasets.Value("int32"),
-        "has_survived_5_years": 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 HabermanConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(HabermanConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Haberman(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "survival"
-    BUILDER_CONFIGS = [
-        HabermanConfig(name="survival",
-                       description="Haberman 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