Datasets:
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"""PageBlocks Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {
}
DESCRIPTION = "PageBlocks 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_page_blocks_classification_78,
author = {Malerba,Donato},
title = {{Page Blocks Classification}},
year = {1995},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5J590}}
}
"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/page_blocks/raw/main/page_blocks.data"
}
features_types_per_config = {
"page_blocks": {
"height": datasets.Value("float64"),
"lenght": datasets.Value("float64"),
"area": datasets.Value("float64"),
"eccentricity": datasets.Value("float64"),
"percentage_black_pixels": datasets.Value("float64"),
"percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
"mean_numer_of_transitions": datasets.Value("float64"),
"number_of_black_pixels": datasets.Value("float64"),
"number_of_black_pixels_after_rlsa": datasets.Value("float64"),
"number_of_transitions": datasets.Value("int8")
},
"page_blocks_binary": {
"height": datasets.Value("float64"),
"lenght": datasets.Value("float64"),
"area": datasets.Value("float64"),
"eccentricity": datasets.Value("float64"),
"percentage_black_pixels": datasets.Value("float64"),
"percentage_black_pixels_after_rlsa_and": datasets.Value("float64"),
"mean_numer_of_transitions": datasets.Value("float64"),
"number_of_black_pixels": datasets.Value("float64"),
"number_of_black_pixels_after_rlsa": datasets.Value("float64"),
"has_multiple_transitions": datasets.ClassLabel(num_classes=2)
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class PageBlocksConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(PageBlocksConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class PageBlocks(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "page_blocks"
BUILDER_CONFIGS = [
PageBlocksConfig(name="page_blocks",
description="PageBlocks for regression."),
PageBlocksConfig(name="page_blocks_binary",
description="PageBlocks 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:
if self.config.name == "page_blocks_binary":
data["number_of_transitions"] = data["number_of_transitions"].apply(lambda x: 1 if x > 1 else 0)
data = data.rename(columns={"number_of_transitions": "has_multiple_transitions"})
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
data = data.reset_index()
data.drop("index", axis="columns", inplace=True)
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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