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README.md

@@ -1,3 +1,21 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- hypo
+- tabular_classification
+- binary_classification
+pretty_name: Hypo
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- hypo
 ---
+# Hypo
+The Hypo dataset.
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**
+|-----------------------|---------------------------|
+| hypo                  | Multiclass classification.| What kind of hypothyroidism does the patient have? |
+| has_hypo              | Binary classification.| Does the patient hypothyroidism does the patient have? |

hypo.py

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+"""Hypo Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"negative": 0,
+	"compensated hypothyroid": 1,
+	"primary hypothyroid": 2
+}
+
+DESCRIPTION = "Hypo dataset."
+_HOMEPAGE = ""
+_URLS = ("")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/hypo/resolve/main/hypo.data"
+}
+features_types_per_config = {
+	"hypo": {
+		"age": datasets.Value("int8"),
+		"sex": datasets.Value("string"),
+		"on_thyroxine": datasets.Value("boolS"),
+		"query_on_thyroxine": datasets.Value("bool"),
+		"on_antithyroid_medication": datasets.Value("bool"),
+		"sick": datasets.Value("bool"),
+		"pregnant": datasets.Value("bool"),
+		"thyroid_surgery": datasets.Value("bool"),
+		"I131_treatment": datasets.Value("bool"),
+		"query_hypothyroid": datasets.Value("bool"),
+		"query_hyperthyroid": datasets.Value("bool"),
+		"lithium": datasets.Value("bool"),
+		"goitre": datasets.Value("bool"),
+		"tumor": datasets.Value("bool"),
+		"hypopituitary": datasets.Value("bool"),
+		"psych": datasets.Value("bool"),
+		"TSH_measured": datasets.Value("bool"),
+		"TSH": datasets.Value("string"),
+		"T3_measured": datasets.Value("bool"),
+		"T3": datasets.Value("float64"),
+		"TT4_measured": datasets.Value("bool"),
+		"TT4": datasets.Value("float64"),
+		"T4U_measured": datasets.Value("bool"),
+		"T4U": datasets.Value("float64"),
+		"FTI_measured": datasets.Value("bool"),
+		"FTI": datasets.Value("float64"),
+		"TBG_measured": datasets.Value("string"),
+		"referral_source": datasets.Value("string"),
+		"class": datasets.ClassLabel(num_classes=3, names=("negative", "compensated hypothyroid", "primary hypothyroid"))
+	},
+	"has_hypo": {
+		"age": datasets.Value("int8"),
+		"sex": datasets.Value("string"),
+		"on_thyroxine": datasets.Value("boolS"),
+		"query_on_thyroxine": datasets.Value("bool"),
+		"on_antithyroid_medication": datasets.Value("bool"),
+		"sick": datasets.Value("bool"),
+		"pregnant": datasets.Value("bool"),
+		"thyroid_surgery": datasets.Value("bool"),
+		"I131_treatment": datasets.Value("bool"),
+		"query_hypothyroid": datasets.Value("bool"),
+		"query_hyperthyroid": datasets.Value("bool"),
+		"lithium": datasets.Value("bool"),
+		"goitre": datasets.Value("bool"),
+		"tumor": datasets.Value("bool"),
+		"hypopituitary": datasets.Value("bool"),
+		"psych": datasets.Value("bool"),
+		"TSH_measured": datasets.Value("bool"),
+		"TSH": datasets.Value("string"),
+		"T3_measured": datasets.Value("bool"),
+		"T3": datasets.Value("string"),
+		"TT4_measured": datasets.Value("bool"),
+		"TT4": datasets.Value("float64"),
+		"T4U_measured": datasets.Value("bool"),
+		"T4U": datasets.Value("float64"),
+		"FTI_measured": datasets.Value("bool"),
+		"FTI": datasets.Value("float64"),
+		"TBG_measured": datasets.Value("string"),
+		"referral_source": datasets.Value("string"),
+		"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+	},
+}
+features_types_per_config["hypo"]["class"] = datasets.ClassLabel(num_classes=2)
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class HypoConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(HypoConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Hypo(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "hypo"
+	BUILDER_CONFIGS = [
+		HypoConfig(name="hypo", description="Hypo for multiclass classification."),
+		HypoConfig(name="has_hypo", description="Hypo 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 = 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:
+		data.drop("id", axid="columns", inplace=True)
+		data.drop("TBG", axid="columns", inplace=True)
+
+		data = data[data.age != "?"]
+		data = data[data.sex != "?"]
+		data = data[data.TSH != "?"]
+
+		data.loc[data.TT4 == "?", "T3"] = -1
+		data.loc[data.TT4 == "?", "TT4"] = -1
+		data.loc[data.TT4 == "?", "T4U"] = -1
+		data.loc[data.TT4 == "?", "FTI"] = -1
+
+		data = data.infer_objects()
+
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data[feature] = data[feature].apply(encoding_function)
+		
+		if self.config.name == "has_hypo":
+			data["class"] = data["class"].apply(lambda x: 0 if x == 0 else 1)
+				
+		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}")