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README.md

@@ -1,3 +1,24 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- nursery
+- tabular_classification
+pretty_name: Nursery
+size_categories:
+- 1K<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- nursery
+- nursery_binary
 ---
+# Nursery
+The [Nursery dataset](https://archive-beta.ics.uci.edu/dataset/76/nursery) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+Should the nursery school accept the student application?
+
+# Configurations and tasks
+| **Configuration** | **Task**                  |
+|-------------------|---------------------------|
+| nursery            | Multiclass classification |
+| nursery_binary     | Binary classification     |

nursery.py

@@ -0,0 +1,111 @@
+"""Nursery Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+
+DESCRIPTION = "Nursery dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/69/molecular+biology+nursery+junction+gene+sequences"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/69/molecular+biology+nursery+junction+gene+sequences")
+_CITATION = """
+@misc{misc_nursery_76,
+  author       = {Rajkovic,Vladislav},
+  title        = {{Nursery}},
+  year         = {1997},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5P88W}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/nursery/raw/main/nursery.data"
+}
+features_types_per_config = {
+    "nursery": {
+		"parents_attitude": datasets.Value("string"),
+		"current_nursery_status": datasets.Value("string"),
+		"form": datasets.Value("string"),
+		"number_of_children": datasets.Value("string"),
+		"housing_status": datasets.Value("string"),
+		"is_family_financially_stable": datasets.Value("bool"),
+		"social_status": datasets.Value("string"),
+		"health_status": datasets.Value("string"),
+        "recommendation": datasets.ClassLabel(num_classes=3, names=("not recommended", "recommended", "priority recommendation"))
+    },
+	"nursery_binary": {
+		"parents_attitude": datasets.Value("string"),
+		"current_nursery_status": datasets.Value("string"),
+		"form": datasets.Value("string"),
+		"number_of_children": datasets.Value("string"),
+		"housing_status": datasets.Value("string"),
+		"is_family_financially_stable": datasets.Value("bool"),
+		"social_status": datasets.Value("string"),
+		"health_status": datasets.Value("string"),
+        "recommendation": 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 NurseryConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(NurseryConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Nursery(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "nursery"
+    BUILDER_CONFIGS = [
+        NurseryConfig(name="nursery",
+                   description="Nursery for multiclass classification."),
+        NurseryConfig(name="nursery_binary",
+                   description="Nursery 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)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row
+
+    def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
+        if self.config.name == "nursery_binary":
+            data["recommendation"] = data["recommendation"].apply(lambda x: 1 if x > 0 else 0)
+
+        for feature in _ENCODING_DICS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
+                
+        return data[list(features_types_per_config[self.config.name].keys())]
+
+    def encode(self, feature, value):
+        if feature in _ENCODING_DICS:
+            return _ENCODING_DICS[feature][value]
+        raise ValueError(f"Unknown feature: {feature}")