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README.md

@@ -1,3 +1,29 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- waveformnoiseV1
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: WaveformNoiseV1
+size_categories:
+- 1K<n<5K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- waveformnoiseV1
+- waveformnoiseV1_0
+- waveformnoiseV1_1
+- waveformnoiseV1_2
 ---
+# WaveformNoiseV1
+The [WaveformNoiseV1 dataset](https://archive-beta.ics.uci.edu/dataset/107/waveform+database+generator+version+1) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| waveformnoiseV1                 | Multiclass classification.|                         |
+| waveformnoiseV1_0               | Binary classification.    | Is the image of class 0? |
+| waveformnoiseV1_1               | Binary classification.    | Is the image of class 1? |
+| waveformnoiseV1_2               | Binary classification.    | Is the image of class 2? |

waveform_noise.py

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+"""WaveformNoiseV1 Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+
+DESCRIPTION = "WaveformNoiseV1 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_waveform_database_generator_(version_1)_107,
+  author       = {Breiman,L. & Stone,C.J.},
+  title        = {{Waveform Database Generator (Version 1)}},
+  year         = {1988},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5CS3C}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/waveformnoiseV1/raw/main/data.csv"
+}
+features_types_per_config = {
+	"waveformnoiseV1": {f"feature_{i}": datasets.Value("float64") for i in range(data.shape[1] - 1)},
+	"waveformnoiseV1_0": {f"feature_{i}": datasets.Value("float64") for i in range(data.shape[1] - 1)},
+	"waveformnoiseV1_1": {f"feature_{i}": datasets.Value("float64") for i in range(data.shape[1] - 1)},
+	"waveformnoiseV1_2": {f"feature_{i}": datasets.Value("float64") for i in range(data.shape[1] - 1)},
+}
+
+features_types_per_config["waveformnoiseV1"]["class"] = datasets.ClassLabel(num_classes=3)
+features_types_per_config["waveformnoiseV1_0"]["class"] = datasets.ClassLabel(num_classes=2)
+features_types_per_config["waveformnoiseV1_1"]["class"] = datasets.ClassLabel(num_classes=2)
+features_types_per_config["waveformnoiseV1_2"]["class"] = datasets.ClassLabel(num_classes=2)
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class WaveformNoiseV1Config(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(WaveformNoiseV1Config, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class WaveformNoiseV1(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "waveformnoiseV1"
+	BUILDER_CONFIGS = [
+		WaveformNoiseV1Config(name="waveformnoiseV1", description="WaveformNoiseV1 for multiclass classification."),
+		WaveformNoiseV1Config(name="waveformnoiseV1_0", description="WaveformNoiseV1 for binary classification."),
+		WaveformNoiseV1Config(name="waveformnoiseV1_1", description="WaveformNoiseV1 for binary classification."),
+		WaveformNoiseV1Config(name="waveformnoiseV1_2", description="WaveformNoiseV1 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, header=None)
+		data.columns = [f"feature_{i}" for i in range(data.shape[1] - 1)] + ["class"]
+		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 == "waveformnoiseV1_0":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
+		elif self.config.name == "waveformnoiseV1_1":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+		elif self.config.name == "waveformnoiseV1_2":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data.loc[:, feature] = data[feature].apply(encoding_function)
+				
+		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}")