updated to datasets 4.*

README.md

@@ -1,20 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: blood
+  data_files:
+  - path: blood/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Blood
+size_categories: 1M<n<10M
 tags:
-- blood
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: Blood Transfusion
-size_categories:
-- n<1K
 task_categories:
 - tabular-classification
-configs:
-- blood
-license: cc
 ---
 # Blood
 The [Blood Transfusion dataset](https://archive-beta.ics.uci.edu/dataset/176/blood+transfusion+service+center) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

blood.py

@@ -1,92 +0,0 @@
-"""Blood"""
-
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-_BASE_FEATURE_NAMES = [
-    "months_since_last_donation",
-    "total_donation",
-    "total_blood_donated_in_cc",
-    "months_since_last_donation",
-    "has_donated_last_month"
-]
-
-DESCRIPTION = "Blood dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Blood"
-_URLS = ("https://huggingface.co/datasets/mstz/blood/raw/blood.csv")
-_CITATION = """
-@misc{misc_blood_transfusion_service_center_176,
-  author       = {Yeh,I-Cheng},
-  title        = {{Blood Transfusion Service Center}},
-  year         = {2008},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5GS39}}
-}"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/blood/raw/main/transfusion.data"
-}
-features_types_per_config = {
-    "blood": {
-        "months_since_last_donation": datasets.Value("int64"),
-        "total_donation": datasets.Value("int64"),
-        "total_blood_donated_in_cc": datasets.Value("int64"),
-        "months_since_last_donation": datasets.Value("int64"),
-        "has_donated_last_month": 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 BloodConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(BloodConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Blood(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "blood"
-    BUILDER_CONFIGS = [
-        BloodConfig(name="blood", description="Blood 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
-
-        data = data.astype({
-            "months_since_last_donation": "int64",
-            "total_donation": "int64",
-            "total_blood_donated_in_cc": "int64",
-            "months_since_last_donation": "int64",
-            "has_donated_last_month": "int64"
-        })        
-
-        for row_id, row in data.iterrows():
-            data_row = dict(row)
-            
-            if isinstance(data_row["months_since_last_donation"], pandas.Series):
-                data_row["months_since_last_donation"] = data_row["months_since_last_donation"].values[0]
-
-            yield row_id, data_row

blood/train.csv

@@ -0,0 +1,749 @@
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