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README.md

@@ -1,3 +1,30 @@
 ---
-license: cc-by-4.0
+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

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