Datasets:
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"""Planning: A Census Dataset"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"V1",
"V2",
"V3",
"V4",
"V5",
"V6",
"V7",
"V8",
"V9",
"V10",
"V11",
"V12",
"class"
]
DESCRIPTION = "Planning dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Planning"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Planning")
_CITATION = """
@misc{misc_planning_relax_230,
author = {Bhatt,Rajen},
title = {{Planning Relax}},
year = {2012},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5T023}}
}"""
# Dataset info
urls_per_split = {
"planning": {"train": "https://huggingface.co/datasets/mstz/planning/raw/main/plrx.csv"}
}
features_types_per_config = {
"planning": {
"V1": datasets.Value("float64"),
"V2": datasets.Value("float64"),
"V3": datasets.Value("float64"),
"V4": datasets.Value("float64"),
"V5": datasets.Value("float64"),
"V6": datasets.Value("float64"),
"V7": datasets.Value("float64"),
"V8": datasets.Value("float64"),
"V9": datasets.Value("float64"),
"V10": datasets.Value("float64"),
"V11": datasets.Value("float64"),
"V12": 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}
classPlanningConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(PlanningConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
classPlanning(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "planning"
BUILDER_CONFIGS = [
PlanningConfig(name="planning",
description="Planning 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[self.config.name]["train"]})
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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