Update parquet files

README.md

@@ -1,30 +0,0 @@
----
-language:
-- en
-tags:
-- spambase
-- tabular_classification
-- binary_classification
-pretty_name: Spambase
-size_categories:
-- 1K<n<10K
-task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
-- tabular-classification
-configs:
-- spambase
----
-# Spambase
-The [Spambase dataset](https://archive.ics.uci.edu/ml/datasets/Spambase) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
-
-# Configurations and tasks
-| **Configuration** | **Task**                  | **Description**  |
-|-------------------|---------------------------|------------------|
-| spambase          | Binary classification     | Is the mail spam?|
-
-
-# Usage
-```python
-from datasets import load_dataset
-
-dataset = load_dataset("mstz/spambase", "spambase")["train"]
-```

spambase.py

@@ -1,127 +0,0 @@
-"""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

spambase/spambase-train.parquet

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