Datasets:
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"""Yeast Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {
"class": {
"MIT": 0,
"NUC": 1,
"CYT": 2,
"ME1": 3,
"EXC": 4,
"ME2": 5,
"ME3": 6,
"VAC": 7,
"POX": 8,
"ERL": 9
}
}
DESCRIPTION = "Yeast dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/110/yeast"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/110/yeast")
_CITATION = """
@misc{misc_yeast_110,
author = {Nakai,Kenta},
title = {{Yeast}},
year = {1996},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5KG68}}
}
"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/yeast/raw/main/yeast.csv"
}
features_types_per_config = {
"yeast": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=10)
},
"yeast_0": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_1": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_2": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_3": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_4": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_5": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_6": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_7": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_8": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2)
},
"yeast_9": {
"mcg": datasets.Value("float64"),
"gvh": datasets.Value("float64"),
"alm": datasets.Value("float64"),
"mit": datasets.Value("float64"),
"erl": datasets.Value("bool"),
"pox": datasets.Value("float64"),
"vac": datasets.Value("float64"),
"nuc": datasets.Value("float64"),
"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 YeastConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(YeastConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Yeast(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "yeast"
BUILDER_CONFIGS = [
YeastConfig(name="yeast", description="Yeast for multiclass classification."),
YeastConfig(name="yeast_0", description="Yeast for binary classification."),
YeastConfig(name="yeast_1", description="Yeast for binary classification."),
YeastConfig(name="yeast_2", description="Yeast for binary classification."),
YeastConfig(name="yeast_3", description="Yeast for binary classification."),
YeastConfig(name="yeast_4", description="Yeast for binary classification."),
YeastConfig(name="yeast_5", description="Yeast for binary classification."),
YeastConfig(name="yeast_6", description="Yeast for binary classification."),
YeastConfig(name="yeast_7", description="Yeast for binary classification."),
YeastConfig(name="yeast_8", description="Yeast for binary classification."),
YeastConfig(name="yeast_9", description="Yeast 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:
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
data["erl"] = data["erl"].apply(lambda x: True if x == 1 else False)
data = data.astype({"erl": "bool"})
if self.config.name == "yeast_0":
data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
elif self.config.name == "yeast_1":
data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
elif self.config.name == "yeast_2":
data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
elif self.config.name == "yeast_3":
data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
elif self.config.name == "yeast_4":
data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
elif self.config.name == "yeast_5":
data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
elif self.config.name == "yeast_6":
data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
elif self.config.name == "yeast_7":
data["class"] = data["class"].apply(lambda x: 1 if x == 7 else 0)
elif self.config.name == "yeast_8":
data["class"] = data["class"].apply(lambda x: 1 if x == 8 else 0)
elif self.config.name == "yeast_9":
data["class"] = data["class"].apply(lambda x: 1 if x == 9 else 0)
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}")
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