Datasets:
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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/waveform_noise_v1/raw/main/data.csv"
}
features_types_per_config = {
"waveformnoiseV1": {f"feature_{i}": datasets.Value("float64") for i in range(40)},
"waveformnoiseV1_0": {f"feature_{i}": datasets.Value("float64") for i in range(40)},
"waveformnoiseV1_1": {f"feature_{i}": datasets.Value("float64") for i in range(40)},
"waveformnoiseV1_2": {f"feature_{i}": datasets.Value("float64") for i in range(40)},
}
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}")
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