Datasets:

mstz
/

vertebral_column

 ---
+language:
+- en
+tags:
+- vertebral_column
+- tabular_classification
+- binary_classification
+pretty_name: Vertebral Column
+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:
+- vertebral
 ---
+# Vertebral Column
+The [Vertebral Column dataset](https://archive.ics.uci.edu/ml/datasets/vertebral+column) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**         |
+|-------------------|---------------------------|-------------------------|
+| abnormal          | Binary classification     | Is the spine abnormal?|
+# Usage
+```python
+from datasets import load_dataset
+dataset = load_dataset("mstz/vertebral", "abnormal")["train"]
+```

data.csv ADDED Viewed

	@@ -0,0 +1,311 @@

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vertebral_column.py ADDED Viewed

	@@ -0,0 +1,78 @@

+"""VertebralColumn"""
+from typing import List
+import datasets
+import pandas
+VERSION = datasets.Version("1.0.0")
+DESCRIPTION = "VertebralColumn dataset from the UCI ML repository."
+_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/VertebralColumn"
+_URLS = ("https://archive.ics.uci.edu/ml/datasets/VertebralColumn")
+_CITATION = """
+@misc{misc_vertebral_column_212,
+  author       = {Barreto,Guilherme & Neto,Ajalmar},
+  title        = {{Vertebral Column}},
+  year         = {2011},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5K89B}}
+}"""
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/vertebral_column/raw/main/data.csv"
+}
+features_types_per_config = {
+    "abnormal": {
+	    "pelvic_incidence": datasets.Value("float64"),
+		"pelvic_tilt": datasets.Value("float64"),
+		"lumbar_lordosis_angle": datasets.Value("float64"),
+		"sacral_slope": datasets.Value("float64"),
+		"pelvic_radius": datasets.Value("float64"),
+		"degree_spondylolisthesis": datasets.Value("float64"),
+		"is_abnormal": 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 VertebralColumnConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(VertebralColumnConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+class VertebralColumn(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "abnormal"
+    BUILDER_CONFIGS = [
+        VertebralColumnConfig(name="abnormal",
+                    description="VertebralColumn 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