Delete waveform_noise.py

waveform_noise.py

@@ -1,104 +0,0 @@
-"""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}")