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README.md

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

haberman.data

@@ -0,0 +1,307 @@
+age,year_of_operation,number_of_axillary_nodes,has_survived_5_years
+30,1964,1,1
+30,1962,3,1
+30,1965,0,1
+31,1959,2,1
+31,1965,4,1
+33,1958,10,1
+33,1960,0,1
+34,1959,0,0
+34,1966,9,0
+34,1958,30,1
+34,1960,1,1
+34,1961,10,1
+34,1967,7,1
+34,1960,0,1
+35,1964,13,1
+35,1963,0,1
+36,1960,1,1
+36,1969,0,1
+37,1960,0,1
+37,1963,0,1
+37,1958,0,1
+37,1959,6,1
+37,1960,15,1
+37,1963,0,1
+38,1969,21,0
+38,1959,2,1
+38,1960,0,1
+38,1960,0,1
+38,1962,3,1
+38,1964,1,1
+38,1966,0,1
+38,1966,11,1
+38,1960,1,1
+38,1967,5,1
+39,1966,0,0
+39,1963,0,1
+39,1967,0,1
+39,1958,0,1
+39,1959,2,1
+39,1963,4,1
+40,1958,2,1
+40,1958,0,1
+40,1965,0,1
+41,1960,23,0
+41,1964,0,0
+41,1967,0,0
+41,1958,0,1
+41,1959,8,1
+41,1959,0,1
+41,1964,0,1
+41,1969,8,1
+41,1965,0,1
+41,1965,0,1
+42,1969,1,0
+42,1959,0,0
+42,1958,0,1
+42,1960,1,1
+42,1959,2,1
+42,1961,4,1
+42,1962,20,1
+42,1965,0,1
+42,1963,1,1
+43,1958,52,0
+43,1959,2,0
+43,1964,0,0
+43,1964,0,0
+43,1963,14,1
+43,1964,2,1
+43,1964,3,1
+43,1960,0,1
+43,1963,2,1
+43,1965,0,1
+43,1966,4,1
+44,1964,6,0
+44,1958,9,0
+44,1963,19,0
+44,1961,0,1
+44,1963,1,1
+44,1961,0,1
+44,1967,16,1
+45,1965,6,0
+45,1966,0,0
+45,1967,1,0
+45,1960,0,1
+45,1967,0,1
+45,1959,14,1
+45,1964,0,1
+45,1968,0,1
+45,1967,1,1
+46,1958,2,0
+46,1969,3,0
+46,1962,5,0
+46,1965,20,0
+46,1962,0,1
+46,1958,3,1
+46,1963,0,1
+47,1963,23,0
+47,1962,0,0
+47,1965,0,0
+47,1961,0,1
+47,1963,6,1
+47,1966,0,1
+47,1967,0,1
+47,1958,3,1
+47,1960,4,1
+47,1968,4,1
+47,1966,12,1
+48,1958,11,0
+48,1958,11,0
+48,1967,7,0
+48,1961,8,1
+48,1962,2,1
+48,1964,0,1
+48,1966,0,1
+49,1963,0,0
+49,1964,10,0
+49,1961,1,1
+49,1962,0,1
+49,1966,0,1
+49,1960,1,1
+49,1962,1,1
+49,1963,3,1
+49,1961,0,1
+49,1967,1,1
+50,1963,13,0
+50,1964,0,0
+50,1959,0,1
+50,1961,6,1
+50,1961,0,1
+50,1963,1,1
+50,1958,1,1
+50,1959,2,1
+50,1961,0,1
+50,1964,0,1
+50,1965,4,1
+50,1966,1,1
+51,1959,13,0
+51,1959,3,0
+51,1964,7,1
+51,1959,1,1
+51,1965,0,1
+51,1966,1,1
+52,1969,3,0
+52,1959,2,0
+52,1962,3,0
+52,1966,4,0
+52,1961,0,1
+52,1963,4,1
+52,1969,0,1
+52,1960,4,1
+52,1960,5,1
+52,1962,0,1
+52,1962,1,1
+52,1964,0,1
+52,1965,0,1
+52,1968,0,1
+53,1958,4,0
+53,1965,1,0
+53,1959,3,0
+53,1960,9,0
+53,1963,24,0
+53,1965,12,0
+53,1958,1,1
+53,1960,1,1
+53,1960,2,1
+53,1961,1,1
+53,1963,0,1
+54,1960,11,0
+54,1965,23,0
+54,1965,5,0
+54,1968,7,0
+54,1959,7,1
+54,1960,3,1
+54,1966,0,1
+54,1967,46,1
+54,1962,0,1
+54,1969,7,1
+54,1963,19,1
+54,1958,1,1
+54,1962,0,1
+55,1963,6,0
+55,1968,15,0
+55,1958,1,1
+55,1958,0,1
+55,1958,1,1
+55,1966,18,1
+55,1966,0,1
+55,1969,3,1
+55,1969,22,1
+55,1967,1,1
+56,1965,9,0
+56,1966,3,0
+56,1960,0,1
+56,1966,2,1
+56,1966,1,1
+56,1967,0,1
+56,1960,0,1
+57,1961,5,0
+57,1962,14,0
+57,1964,1,0
+57,1964,9,1
+57,1969,0,1
+57,1961,0,1
+57,1962,0,1
+57,1963,0,1
+57,1964,0,1
+57,1964,0,1
+57,1967,0,1
+58,1959,0,1
+58,1960,3,1
+58,1961,1,1
+58,1967,0,1
+58,1958,0,1
+58,1958,3,1
+58,1961,2,1
+59,1962,35,0
+59,1960,0,1
+59,1963,0,1
+59,1964,1,1
+59,1964,4,1
+59,1964,0,1
+59,1964,7,1
+59,1967,3,1
+60,1959,17,0
+60,1965,0,0
+60,1961,1,1
+60,1967,2,1
+60,1961,25,1
+60,1964,0,1
+61,1962,5,0
+61,1965,0,0
+61,1968,1,0
+61,1959,0,1
+61,1959,0,1
+61,1964,0,1
+61,1965,8,1
+61,1968,0,1
+61,1959,0,1
+62,1959,13,0
+62,1958,0,0
+62,1965,19,0
+62,1962,6,1
+62,1966,0,1
+62,1966,0,1
+62,1958,0,1
+63,1960,1,0
+63,1961,0,1
+63,1962,0,1
+63,1963,0,1
+63,1963,0,1
+63,1966,0,1
+63,1961,9,1
+63,1961,28,1
+64,1958,0,1
+64,1965,22,1
+64,1966,0,1
+64,1961,0,1
+64,1968,0,1
+65,1958,0,0
+65,1961,2,0
+65,1962,22,0
+65,1966,15,0
+65,1958,0,1
+65,1964,0,1
+65,1967,0,1
+65,1959,2,1
+65,1964,0,1
+65,1967,1,1
+66,1958,0,0
+66,1961,13,0
+66,1958,0,1
+66,1958,1,1
+66,1968,0,1
+67,1964,8,0
+67,1963,1,0
+67,1966,0,1
+67,1966,0,1
+67,1961,0,1
+67,1965,0,1
+68,1967,0,1
+68,1968,0,1
+69,1967,8,0
+69,1960,0,1
+69,1965,0,1
+69,1966,0,1
+70,1958,0,0
+70,1958,4,0
+70,1966,14,1
+70,1967,0,1
+70,1968,0,1
+70,1959,8,1
+70,1963,0,1
+71,1968,2,1
+72,1963,0,0
+72,1958,0,1
+72,1964,0,1
+72,1967,3,1
+73,1962,0,1
+73,1968,0,1
+74,1965,3,0
+74,1963,0,1
+75,1962,1,1
+76,1967,0,1
+77,1965,3,1
+78,1965,1,0
+83,1958,2,0

haberman.py

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+"""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