updated to datasets 4.*

README.md

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

pima.csv

@@ -1,769 +0,0 @@
-number_of_pregnancies,plasma_glucose_concentration,diastolic_blood_pressure,triceps_thickness,serum_insulin,bmi,diabetes_pedigree,age,class
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-0,137,40,35,168,43.1,2.288,33,1
-5,116,74,0,0,25.6,0.201,30,0
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-8,125,96,0,0,0,0.232,54,1
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-10,168,74,0,0,38,0.537,34,1
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-1,189,60,23,846,30.1,0.398,59,1
-5,166,72,19,175,25.8,0.587,51,1
-7,100,0,0,0,30,0.484,32,1
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-8,99,84,0,0,35.4,0.388,50,0
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-9,119,80,35,0,29,0.263,29,1
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-7,147,76,0,0,39.4,0.257,43,1
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pima.py

@@ -1,72 +0,0 @@
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-
-DESCRIPTION = "Pima dataset from the OpenML repository."
-_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=37"
-_URLS = ("https://www.openml.org/search?type=data&status=active&id=37")
-_CITATION = """"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/pima/raw/main/pima.csv"
-}
-features_types_per_config = {
-    "pima": {
-	    "number_of_pregnancies": datasets.Value("int8"),
-		"plasma_glucose_concentration": datasets.Value("float64"),
-		"diastolic_blood_pressure": datasets.Value("float64"),
-		"triceps_thickness": datasets.Value("float64"),
-		"serum_insulin": datasets.Value("float64"),
-		"bmi": datasets.Value("float64"),
-		"diabetes_pedigree": datasets.Value("float64"),
-		"age": datasets.Value("float64"),
-		"has_diabetes": 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 PimaConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(PimaConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Pima(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "pima"
-    BUILDER_CONFIGS = [
-        PimaConfig(name="pima",
-                    description="Pima 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)
-        data = data.rename(columns={"class": "has_diabetes"})
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-
-            yield row_id, data_row

pima/train.csv

@@ -0,0 +1,769 @@
+number_of_pregnancies,plasma_glucose_concentration,diastolic_blood_pressure,triceps_thickness,serum_insulin,bmi,diabetes_pedigree,age,has_diabetes
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