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Datasets:
mstz
/
spambase

Tasks:
Tabular Classification
Modalities:
Tabular
Languages:
English
Size:
1K - 10K
Tags:
spambase
tabular_classification
binary_classification
UCI
Libraries:
Datasets
License:
Dataset card Viewer Files Community
spambase
File size: 4,913 Bytes 
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"""Spambase: A Census Dataset"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")

DESCRIPTION = "Spambase dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Spambase"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/Spambase")
_CITATION = """
@misc{misc_spambase_94,
  author       = {Hopkins,Mark, Reeber,Erik, Forman,George & Suermondt,Jaap},
  title        = {{Spambase}},
  year         = {1999},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C53G6X}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/spambase/raw/main/spambase.data"
}
features_types_per_config = {
    "spambase": {
	   	"word_freq_make": datasets.Value("float64"),
		"word_freq_address": datasets.Value("float64"),
		"word_freq_all": datasets.Value("float64"),
		"word_freq_3d": datasets.Value("float64"),
		"word_freq_our": datasets.Value("float64"),
		"word_freq_over": datasets.Value("float64"),
		"word_freq_remove": datasets.Value("float64"),
		"word_freq_internet": datasets.Value("float64"),
		"word_freq_order": datasets.Value("float64"),
		"word_freq_mail": datasets.Value("float64"),
		"word_freq_receive": datasets.Value("float64"),
		"word_freq_will": datasets.Value("float64"),
		"word_freq_people": datasets.Value("float64"),
		"word_freq_report": datasets.Value("float64"),
		"word_freq_addresses": datasets.Value("float64"),
		"word_freq_free": datasets.Value("float64"),
		"word_freq_business": datasets.Value("float64"),
		"word_freq_email": datasets.Value("float64"),
		"word_freq_you": datasets.Value("float64"),
		"word_freq_credit": datasets.Value("float64"),
		"word_freq_your": datasets.Value("float64"),
		"word_freq_font": datasets.Value("float64"),
		"word_freq_000": datasets.Value("float64"),
		"word_freq_money": datasets.Value("float64"),
		"word_freq_hp": datasets.Value("float64"),
		"word_freq_hpl": datasets.Value("float64"),
		"word_freq_george": datasets.Value("float64"),
		"word_freq_650": datasets.Value("float64"),
		"word_freq_lab": datasets.Value("float64"),
		"word_freq_labs": datasets.Value("float64"),
		"word_freq_telnet": datasets.Value("float64"),
		"word_freq_857": datasets.Value("float64"),
		"word_freq_data": datasets.Value("float64"),
		"word_freq_415": datasets.Value("float64"),
		"word_freq_85": datasets.Value("float64"),
		"word_freq_technology": datasets.Value("float64"),
		"word_freq_1999": datasets.Value("float64"),
		"word_freq_parts": datasets.Value("float64"),
		"word_freq_pm": datasets.Value("float64"),
		"word_freq_direct": datasets.Value("float64"),
		"word_freq_cs": datasets.Value("float64"),
		"word_freq_meeting": datasets.Value("float64"),
		"word_freq_original": datasets.Value("float64"),
		"word_freq_project": datasets.Value("float64"),
		"word_freq_re": datasets.Value("float64"),
		"word_freq_edu": datasets.Value("float64"),
		"word_freq_table": datasets.Value("float64"),
		"word_freq_conference": datasets.Value("float64"),
		"char_freq_;": datasets.Value("float64"),
		"char_freq_(": datasets.Value("float64"),
		"char_freq_[": datasets.Value("float64"),
		"char_freq_!": datasets.Value("float64"),
		"char_freq_$": datasets.Value("float64"),
		"char_freq_#": datasets.Value("float64"),
		"capital_run_length_average": datasets.Value("float64"),
		"capital_run_length_longest": datasets.Value("float64"),
		"capital_run_length_total": datasets.Value("float64"),
		"is_spam": 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 SpambaseConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(SpambaseConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class Spambase(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "spambase"
    BUILDER_CONFIGS = [
        SpambaseConfig(name="spambase",
                    description="Spambase 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)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row