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Indian Liver Patient Dataset (ILPD).csv

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+age,is_male,total_bilirubin,direct_ribilubin,alkaline_phosphotase,alamine_aminotransferasi,aspartate_aminotransferase,total_proteins,albumin,albumin_to_globulin_ratio,class
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README.md

@@ -1,3 +1,31 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- ilpd
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: ILPD
+size_categories:
+- 10K<n<100K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- liver
 ---
+# ILPD
+The [ILPD dataset](https://archive.ics.uci.edu/ml/datasets/ILPD) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**                       |
+|-------------------|---------------------------|---------------------------------------|
+| liver             | Binary classification     | Does the patient have liver problems? |
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/ilpd", "liver")["train"]
+```

liver.py

@@ -0,0 +1,81 @@
+"""ILPD"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+DESCRIPTION = "ILPD dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/ILPD"
+_URLS = ("https://archive.ics.uci.edu/ml/datasets/ILPD")
+_CITATION = """
+@misc{misc_ilpd_(indian_liver_patient_dataset)_225,
+  author       = {Ramana,Bendi & Venkateswarlu,N.},
+  title        = {{ILPD (Indian Liver Patient Dataset)}},
+  year         = {2012},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5D02C}}
+}"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/ilpd/raw/main/Indian Liver Patient Dataset (ILPD).csv"
+}
+features_types_per_config = {
+    "ilpd": {
+        "age": datasets.Value("int8"),
+        "is_male": datasets.Value("bool"),
+        "total_bilirubin": datasets.Value("float64"),
+        "direct_ribilubin": datasets.Value("float64"),
+        "alkaline_phosphotase": datasets.Value("float64"),
+        "alamine_aminotransferasi": datasets.Value("float64"),
+        "aspartate_aminotransferase": datasets.Value("float64"),
+        "total_proteins": datasets.Value("float64"),
+        "albumin": datasets.Value("float64"),
+        "albumin_to_globulin_ratio": datasets.Value("float64"),
+        "class": 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 ILPDConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(ILPDConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class ILPD(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "liver"
+    BUILDER_CONFIGS = [
+        ILPDConfig(name="liver",
+                    description="ILPD 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 = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row