Datasets:
File size: 4,980 Bytes
33f25f6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
"""Seeds Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {}
DESCRIPTION = "Seeds dataset."
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification"
_URLS = ("https://archive-beta.ics.uci.edu/dataset/78/page+blocks+classification")
_CITATION = """
@misc{misc_seeds_236,
author = {Charytanowicz,Magorzata, Niewczas,Jerzy, Kulczycki,Piotr, Kowalski,Piotr & Lukasik,Szymon},
title = {{seeds}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5H30K}}
}
"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co./datasets/mstz/seeds/raw/main/seeds.csv"
}
features_types_per_config = {
"seeds": {
"area": datasets.Value("float64"),
"perimeter": datasets.Value("float64"),
"compactness": datasets.Value("float64"),
"length": datasets.Value("float64"),
"width": datasets.Value("float64"),
"asymmetry": datasets.Value("float64"),
"length_grove": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=3),
},
"seeds_0": {
"area": datasets.Value("float64"),
"perimeter": datasets.Value("float64"),
"compactness": datasets.Value("float64"),
"length": datasets.Value("float64"),
"width": datasets.Value("float64"),
"asymmetry": datasets.Value("float64"),
"length_grove": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2),
},
"seeds_1": {
"area": datasets.Value("float64"),
"perimeter": datasets.Value("float64"),
"compactness": datasets.Value("float64"),
"length": datasets.Value("float64"),
"width": datasets.Value("float64"),
"asymmetry": datasets.Value("float64"),
"length_grove": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2),
},
"seeds_2": {
"area": datasets.Value("float64"),
"perimeter": datasets.Value("float64"),
"compactness": datasets.Value("float64"),
"length": datasets.Value("float64"),
"width": datasets.Value("float64"),
"asymmetry": datasets.Value("float64"),
"length_grove": 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 SeedsConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(SeedsConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Seeds(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "seeds"
BUILDER_CONFIGS = [
SeedsConfig(name="seeds", description="Seeds for multiclass classification."),
SeedsConfig(name="seeds_0", description="Seeds for binary classification."),
SeedsConfig(name="seeds_1", description="Seeds for binary classification."),
SeedsConfig(name="seeds_2", description="Seeds 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["class"] = data["class"].apply(lambda x: x - 1)
if self.config.name == "seeds_0":
data["decision"] = data["decision"].apply(lambda x: 1 if x == 0 else 0)
elif self.config.name == "seeds_1":
data["decision"] = data["decision"].apply(lambda x: 1 if x == 1 else 0)
elif self.config.name == "seeds_2":
data["decision"] = data["decision"].apply(lambda x: 1 if x == 2 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}")
|