Datasets:
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from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Iris efficiency dataset from the UCI repository."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/53/iris"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/53/iris")
_CITATION = """
@misc{misc_iris_53,
author = {Fisher,R. A. & Fisher,R.A.},
title = {{Iris}},
year = {1988},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C56C76}}
}"""
# Dataset info
_BASE_FEATURE_NAMES = [
"sepal_length",
"sepal_width",
"petal_length",
"petal_width",
"class"
]
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/iris/raw/main/iris.data"
}
features_types_per_config = {
"iris": {
"sepal_length": datasets.Value("float64"),
"sepal_width": datasets.Value("float64"),
"petal_length": datasets.Value("float64"),
"petal_width": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=3, names=("setosa", "versicolor", "virginica"))
},
"setosa": {
"sepal_length": datasets.Value("float64"),
"sepal_width": datasets.Value("float64"),
"petal_length": datasets.Value("float64"),
"petal_width": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"versicolor": {
"sepal_length": datasets.Value("float64"),
"sepal_width": datasets.Value("float64"),
"petal_length": datasets.Value("float64"),
"petal_width": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
"virginica": {
"sepal_length": datasets.Value("float64"),
"sepal_width": datasets.Value("float64"),
"petal_length": datasets.Value("float64"),
"petal_width": datasets.Value("float64"),
"class": 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 IrisConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(IrisConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Iris(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "iris"
BUILDER_CONFIGS = [
IrisConfig(name="iris", description="Iris dataset."),
IrisConfig(name="setosa", description="Binary classification of setosa."),
IrisConfig(name="versicolor", description="Binary classification of versicolor."),
IrisConfig(name="virginica", description="Binary classification of virginica.")
]
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.columns = _BASE_FEATURE_NAMES
data.loc[:, "class"] = data["class"].apply(lambda x: {
"Iris-setosa": 0,
"Iris-versicolor": 1,
"Iris-virginica": 2
}[x])
if self.config.name == "setosa":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
elif self.config.name == "versicolor":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
if self.config.name == "virginica":
data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
return data
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