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Datasets:
mstz
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arhythmia

Tasks:
Tabular Classification
Modalities:
Tabular
Formats:
csv
Languages:
English
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< 1K
Tags:
tabular_classification
binary_classification
multiclass_classification
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pandas
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mstz commited on
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Delete arrhythmia.py

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  1. arrhythmia.py +0 -354
arrhythmia.py DELETED
@@ -1,354 +0,0 @@
1
- from typing import List
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- from functools import partial
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-
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- import datasets
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-
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- import pandas
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-
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-
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- VERSION = datasets.Version("1.0.0")
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-
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- DESCRIPTION = "Arhythmia dataset from the UCI ML repository."
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- _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/5/arrhythmia"
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- _URLS = ("https://archive-beta.ics.uci.edu/dataset/5/arrhythmia")
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- _CITATION = """
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- @misc{misc_arrhythmia_5,
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- author = {Guvenir,H., Acar,Burak & Muderrisoglu,Haldun},
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- title = {{Arrhythmia}},
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- year = {1998},
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- howpublished = {UCI Machine Learning Repository},
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- note = {{DOI}: \\url{10.24432/C5BS32}}
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- }"""
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-
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- # Dataset info
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- urls_per_split = {
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- "train": "https://huggingface.co/datasets/mstz/adult/raw/main/arrhythmia.data"
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- }
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- features_types_per_config = {
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- "arrhythmia": {
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- "age": datasets.Value("int8"),
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- "is_female": datasets.Value("bool"),
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- "height": datasets.Value("float64"),
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- "qrs_duration": datasets.Value("float64"),
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- "p_r_interval": datasets.Value("float64"),
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- "q_t_interval": datasets.Value("float64"),
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- "t_interval": datasets.Value("float64"),
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- "p_interval": datasets.Value("float64"),
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- "qrs": datasets.Value("float64"),
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- "t": datasets.Value("float64"),
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- "p": datasets.Value("float64"),
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- "qrst": datasets.Value("float64"),
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- "j": datasets.Value("float64"),
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- "heart_rate": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_DI": datasets.Value("float64"),
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- "average_witdth_of_wave_r_on_DI": datasets.Value("float64"),
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- "average_witdth_of_wave_s_on_DI": datasets.Value("float64"),
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- "average_witdth_of_wave_rprime_on_DI": datasets.Value("float64"),
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- "average_witdth_of_wave_sprime_on_DI": datasets.Value("float64"),
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- "number_of_deflections_on_DI": datasets.Value("float64"),
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- "type_of_ragged_r_wave_on_DI": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_r_wave_on_DI": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_DI": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_p_wave_on_DI": datasets.Value("float64"),
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- "type_of_ragged_t_wave_on_DI": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_DI": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_DII": datasets.Value("float64"),
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- "average_witdth_of_wave_r_on_DII": datasets.Value("float64"),
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- "average_witdth_of_wave_s_on_DII": datasets.Value("float64"),
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- "average_witdth_of_wave_rprime_on_DII": datasets.Value("float64"),
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- "average_witdth_of_wave_sprime_on_DII": datasets.Value("float64"),
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- "number_of_deflections_on_DII": datasets.Value("float64"),
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- "type_of_ragged_r_wave_on_DII": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_r_wave_on_DII": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_DII": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_p_wave_on_DII": datasets.Value("float64"),
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- "type_of_ragged_t_wave_on_DII": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_DII": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_DIII": datasets.Value("float64"),
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- "average_witdth_of_wave_s_on_DIII": datasets.Value("float64"),
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- "average_witdth_of_wave_sprime_on_DIII": datasets.Value("float64"),
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- "number_of_deflections_on_DIII": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_DIII": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_p_wave_on_DIII": datasets.Value("float64"),
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- "type_of_ragged_t_wave_on_DIII": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_DIII": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_AVR": datasets.Value("float64"),
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- "average_witdth_of_wave_r_on_AVR": datasets.Value("float64"),
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- "average_witdth_of_wave_s_on_AVR": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_r_wave_on_AVR": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_AVR": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_p_wave_on_AVR": datasets.Value("float64"),
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- "type_of_ragged_t_wave_on_AVR": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_AVR": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_AVL": datasets.Value("float64"),
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- "average_witdth_of_wave_r_on_AVL": datasets.Value("float64"),
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- "average_witdth_of_wave_s_on_AVL": datasets.Value("float64"),
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- "average_witdth_of_wave_rprime_on_AVL": datasets.Value("float64"),
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- "average_witdth_of_wave_sprime_on_AVL": datasets.Value("float64"),
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- "number_of_deflections_on_AVL": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_AVL": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_AVL": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_AVF": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_p_wave_on_AVF": datasets.Value("float64"),
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- "type_of_diphasic_derivation_of_t_wave_on_AVF": datasets.Value("float64"),
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- "average_witdth_of_wave_q_on_V2": datasets.Value("float64"),
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- "type_of_ragged_p_wave_on_V3": datasets.Value("float64"),
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- "type_of_ragged_t_wave_on_V6": datasets.Value("float64"),
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- "amplitude_of_q_wave_on_DI": datasets.Value("float64"),
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- "amplitude_of_r_wave_on_DI": datasets.Value("float64"),
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- "qrsa_on_DI": datasets.Value("float64"),
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- "amplitude_of_jj_wave_on_DII": datasets.Value("float64"),
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- "qrsa_on_DII": datasets.Value("float64"),
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- "qrsta_on_DII": datasets.Value("float64"),
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- "qrsa_on_V4": datasets.Value("float64"),
286
- "qrsta_on_V4": datasets.Value("float64"),
287
- "amplitude_of_jj_wave_on_V5": datasets.Value("float64"),
288
- "amplitude_of_q_wave_on_V5": datasets.Value("float64"),
289
- "amplitude_of_r_wave_on_V5": datasets.Value("float64"),
290
- "amplitude_of_s_wave_on_V5": datasets.Value("float64"),
291
- "amplitude_of_rprime_wave_on_V5": datasets.Value("float64"),
292
- "amplitude_of_sprime_wave_on_V5": datasets.Value("float64"),
293
- "amplitude_of_p_wave_on_V5": datasets.Value("float64"),
294
- "amplitude_of_t_wave_on_V5": datasets.Value("float64"),
295
- "qrsa_on_V5": datasets.Value("float64"),
296
- "qrsta_on_V5": datasets.Value("float64"),
297
- "amplitude_of_jj_wave_on_V6": datasets.Value("float64"),
298
- "amplitude_of_q_wave_on_V6": datasets.Value("float64"),
299
- "amplitude_of_r_wave_on_V6": datasets.Value("float64"),
300
- "amplitude_of_s_wave_on_V6": datasets.Value("float64"),
301
- "amplitude_of_rprime_wave_on_V6": datasets.Value("float64"),
302
- "amplitude_of_sprime_wave_on_V6": datasets.Value("float64"),
303
- "amplitude_of_p_wave_on_V6": datasets.Value("float64"),
304
- "amplitude_of_t_wave_on_V6": datasets.Value("float64"),
305
- "qrsa_on_V6": datasets.Value("float64"),
306
- "qrsta_on_V6": datasets.Value("float64"),
307
- "type_of_arhythmia": datasets.ClassLabel(num_classes=16, names=["no_arhytmia"] + ["arhytmia_of_type_{f}" for f in range(2, 16)] + ["unknown_arhytmia"])
308
- }
309
- }
310
- features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
311
-
312
-
313
- class ArrhythmiaConfig(datasets.BuilderConfig):
314
- def __init__(self, **kwargs):
315
- super(ArrhythmiaConfig, self).__init__(version=VERSION, **kwargs)
316
- self.features = features_per_config[kwargs["name"]]
317
-
318
-
319
- class Arrhythmia(datasets.GeneratorBasedBuilder):
320
- # dataset versions
321
- DEFAULT_CONFIG = "arhythmia"
322
- BUILDER_CONFIGS = [
323
- ArrhythmiaConfig(name="arhythmia",
324
- description="Multiclass classification of arhythmia type."),
325
- ]
326
-
327
-
328
- def _info(self):
329
- info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
330
- features=features_per_config[self.config.name])
331
-
332
- return info
333
-
334
- def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
335
- downloads = dl_manager.download_and_extract(urls_per_split)
336
-
337
- return [
338
- datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
339
- ]
340
-
341
- def _generate_examples(self, filepath: str):
342
- data = pandas.read_csv(filepath)
343
- data = self.preprocess(data, config=self.config.name)
344
-
345
- for row_id, row in data.iterrows():
346
- data_row = dict(row)
347
-
348
- yield row_id, data_row
349
-
350
- def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
351
- data = data[(data.t != "?") & (data.qrst != "?") & (data.p != "?") & (data.j != "?") & (data.heart_rate != "?")]
352
- data = data.astype({"t": int, "qrst": int, "p": int, "j": int, "heart_rate": int})
353
-
354
- return data[list(features_types_per_config["income"].keys())]