Update parquet files

README.md

@@ -1,21 +0,0 @@
----
-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? |

has_hypo/hypo-train.parquet

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+oid sha256:b153a5335f1c6e9400c0e47fbc83cc51885d8ee5a97e6316ddea8b2b67f6d3f8
+size 65895

hypo.py

@@ -1,168 +0,0 @@
-"""Hypo Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {
-	"class": {
-		"negative": 0,
-		"compensatedhypothyroid": 1,
-		"secondaryhypothyroid": 2,
-		"primaryhypothyroid": 3
-	}
-}
-
-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("int64"),
-		"sex": datasets.Value("string"),
-		"on_thyroxine": datasets.Value("bool"),
-		"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=4,
-									 names=("negative", "compensated hypothyroid", "secondary hypothyroid", "primary hypothyroid"))
-	},
-	"has_hypo": {
-		"age": datasets.Value("int64"),
-		"sex": datasets.Value("string"),
-		"on_thyroxine": datasets.Value("bool"),
-		"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)
-	},
-}
-
-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)
-		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", axis="columns", inplace=True)
-		data.drop("TBG", axis="columns", inplace=True)
-
-		data = data[data.age != "?"]
-		data = data[data.sex != "?"]
-		data = data[data.TSH != "?"]
-
-		data.loc[data.T3 == "?", "T3"] = -1
-		data.loc[data.TT4 == "?", "TT4"] = -1
-		data.loc[data.T4U == "?", "T4U"] = -1
-		data.loc[data.FTI == "?", "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)
-			print("has hypo\n\n\n")
-		
-		print("classes")
-		print(data["class"].unique())
-
-		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}")

hypo/hypo-train.parquet

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