stable_datasets.timeseries package

Submodules

stable_datasets.timeseries.CatsDogs module

class CatsDogs(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

CatsDogs timeseries classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://www.timeseriesclassification.com/description.php?Dataset=CatsDogs', assets=mappingproxy({'train': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/CatsDogs.zip', fallbacks=[], checksum=None, filename=None), 'test': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/CatsDogs.zip', fallbacks=[], checksum=None, filename=None)}), citation='N/A.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.JapaneseVowels module

class JapaneseVowels(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

JapaneseVowels multivariate timeseries classification dataset.

SEQUENCE_LENGTH = 29
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://www.timeseriesclassification.com/description.php?Dataset=JapaneseVowels', assets=mappingproxy({'train': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/JapaneseVowels.zip', fallbacks=[], checksum=None, filename=None), 'test': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/JapaneseVowels.zip', fallbacks=[], checksum=None, filename=None)}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.MosquitoSound module

class MosquitoSound(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

MosquitoSound timeseries classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://www.timeseriesclassification.com/description.php?Dataset=MosquitoSound', assets=mappingproxy({'train': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/MosquitoSound.zip', fallbacks=[], checksum=None, filename=None), 'test': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/MosquitoSound.zip', fallbacks=[], checksum=None, filename=None)}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.Phoneme module

class Phoneme(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Phoneme timeseries classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://www.timeseriesclassification.com/description.php?Dataset=Phoneme', assets=mappingproxy({'train': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/Phoneme.zip', fallbacks=[], checksum=None, filename=None), 'test': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/Phoneme.zip', fallbacks=[], checksum=None, filename=None)}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.RightWhaleCalls module

stable_datasets.timeseries.UrbanSound module

class UrbanSound(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

UrbanSound timeseries classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://www.timeseriesclassification.com/description.php?Dataset=UrbanSound', assets=mappingproxy({'train': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/UrbanSound.zip', fallbacks=[], checksum=None, filename=None), 'test': DownloadInfo(url='http://www.timeseriesclassification.com/Downloads/UrbanSound.zip', fallbacks=[], checksum=None, filename=None)}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.VoiceGenderDetection module

class VoiceGenderDetection(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Voice gender detection dataset derived from VoxCeleb clips.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://dagshub.com/DagsHub/audio-datasets/src/main/voice_gender_detection', assets=mappingproxy({'train': DownloadInfo(url='https://drive.google.com/u/0/uc?id=1HRbWocxwClGy9Fj1MQeugpR4vOaL9ebO&export=download', fallbacks=[], checksum=None, filename='VoxCeleb_gender.zip')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.audiomnist module

class AudioMNIST(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

AudioMNIST spoken-digit classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://github.com/soerenab/AudioMNIST', assets=mappingproxy({'train': DownloadInfo(url='https://github.com/soerenab/AudioMNIST/archive/master.zip', fallbacks=[], checksum=None, filename='AudioMNIST-master.zip')}), citation='@article{audiomnist2023,\n                     title = {AudioMNIST: Exploring Explainable Artificial Intelligence for audio analysis on a simple benchmark},\n                     journal = {Journal of the Franklin Institute},\n                     year = {2023},\n                     issn = {0016-0032},\n                     doi = {https://doi.org/10.1016/j.jfranklin.2023.11.038},\n                     url = {https://www.sciencedirect.com/science/article/pii/S0016003223007536},\n                     author = {Sören Becker and Johanna Vielhaben and Marcel Ackermann and Klaus-Robert Müller and Sebastian Lapuschkin and Wojciech Samek},\n                     keywords = {Deep learning, Neural networks, Interpretability, Explainable artificial intelligence, Audio classification, Speech recognition}}', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.basic_motions module

class BasicMotions(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

BasicMotions multivariate time series classification dataset from the UEA archive.

Smart watch recordings from a student project in which four students performed four activities: the x, y and z axes of the watch’s accelerometer and gyroscope (6 channels), sampled at 10 Hz for 10 seconds (100 steps). The classes are badminton, running, standing and walking. train and test hold 40 recordings each. Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 6
SEQUENCE_LENGTH = 100
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=BasicMotions', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/BasicMotions.zip', fallbacks=[], checksum='sha256:f427609dfe343b1da0dc54ee361f61579e7f18c2a007121740a9f0d90286fac3', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/BasicMotions.zip', fallbacks=[], checksum='sha256:f427609dfe343b1da0dc54ee361f61579e7f18c2a007121740a9f0d90286fac3', filename=None)}), citation='@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.birdvox_dcase_20k module

class BirdVoxDCASE20k(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

BirdVox-DCASE-20k bird-detection dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://wp.nyu.edu/birdvox/birdvox-dcase-20k/', assets=mappingproxy({'audio': DownloadInfo(url='https://zenodo.org/record/1208080/files/BirdVox-DCASE-20k.zip?download=1', fallbacks=['https://zenodo.org/records/1208080/files/BirdVox-DCASE-20k.zip'], checksum=None, filename='BirdVox-DCASE-20k.zip'), 'metadata': DownloadInfo(url='https://ndownloader.figshare.com/files/10853300', fallbacks=[], checksum=None, filename='data_labels.csv')}), citation='@inproceedings{lostanlen2018icassp,\n                     title={BirdVox-full-night: a dataset and benchmark for avian flight call detection},\n                     author={Lostanlen, Vincent and Salamon, Justin and Farnsworth, Andrew and Kelling, Steve and Bello, Juan Pablo},\n                     booktitle={Proc. IEEE ICASSP},\n                     year={2018}}', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.brain_mnist module

BrainMNIST dataset builder stub.

class BrainMNIST(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://mindbigdata.com/opendb/index.html', assets=mappingproxy({}), citation='TBD', license='', checksums=None)
VERSION: Version = Version('0.0.0')

stable_datasets.timeseries.daily_and_sports_activities module

Subject-disjoint windows from UCI’s Daily and Sports Activities dataset.

class DailyAndSportsActivities(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Load 5-second, 45-channel windows without mixing subjects across splits.

The source provides no official train/test partition. This builder defines a reproducible cross-subject split: subjects 1-6 train, subjects 7-8 test. series is time-major (125, 45). Original 1-based subject and segment identifiers are retained; activity labels are zero-based.

CHANNEL_NAMES = ('torso_acc_x', 'torso_acc_y', 'torso_acc_z', 'torso_gyro_x', 'torso_gyro_y', 'torso_gyro_z', 'torso_mag_x', 'torso_mag_y', 'torso_mag_z', 'right_arm_acc_x', 'right_arm_acc_y', 'right_arm_acc_z', 'right_arm_gyro_x', 'right_arm_gyro_y', 'right_arm_gyro_z', 'right_arm_mag_x', 'right_arm_mag_y', 'right_arm_mag_z', 'left_arm_acc_x', 'left_arm_acc_y', 'left_arm_acc_z', 'left_arm_gyro_x', 'left_arm_gyro_y', 'left_arm_gyro_z', 'left_arm_mag_x', 'left_arm_mag_y', 'left_arm_mag_z', 'right_leg_acc_x', 'right_leg_acc_y', 'right_leg_acc_z', 'right_leg_gyro_x', 'right_leg_gyro_y', 'right_leg_gyro_z', 'right_leg_mag_x', 'right_leg_mag_y', 'right_leg_mag_z', 'left_leg_acc_x', 'left_leg_acc_y', 'left_leg_acc_z', 'left_leg_gyro_x', 'left_leg_gyro_y', 'left_leg_gyro_z', 'left_leg_mag_x', 'left_leg_mag_y', 'left_leg_mag_z')
CLASS_NAMES = ('sitting', 'standing', 'lying_on_back', 'lying_on_right_side', 'ascending_stairs', 'descending_stairs', 'standing_in_elevator', 'moving_in_elevator', 'walking_in_parking_lot', 'walking_on_flat_treadmill', 'walking_on_inclined_treadmill', 'running_on_treadmill', 'stepper', 'cross_trainer', 'cycling_horizontal', 'cycling_vertical', 'rowing', 'jumping', 'basketball')
SAMPLE_RATE = 25
SEQUENCE_LENGTH = 125
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://archive.ics.uci.edu/dataset/256/daily+and+sports+activities', assets=mappingproxy({'train': DownloadInfo(url='https://archive.ics.uci.edu/static/public/256/daily+and+sports+activities.zip', fallbacks=[], checksum=None, filename='daily_and_sports_activities.zip'), 'test': DownloadInfo(url='https://archive.ics.uci.edu/static/public/256/daily+and+sports+activities.zip', fallbacks=[], checksum=None, filename='daily_and_sports_activities.zip')}), citation='@misc{daily_and_sports_activities_256,\n  author = {Barshan, Billur and Altun, Kerem},\n  title = {Daily and Sports Activities},\n  year = {2010},\n  publisher = {UCI Machine Learning Repository},\n  doi = {10.24432/C5C59F},\n  url = {https://doi.org/10.24432/C5C59F}\n}', license='CC BY 4.0', checksums=None)
SPLIT_SUBJECTS = {'test': frozenset({7, 8}), 'train': frozenset({1, 2, 3, 4, 5, 6})}
VERSION: Version = Version('1.0.1')

stable_datasets.timeseries.dclde module

DCLDE dataset builder stub.

class DCLDE(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

DCLDE (Detection, Classification, Localization and Density Estimation) bioacoustics dataset. Not implemented yet.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='TBD', assets=mappingproxy({}), citation='TBD', license='', checksums=None)
VERSION: Version = Version('0.0.0')

stable_datasets.timeseries.electricity_hourly module

class ElectricityHourly(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Hourly electricity consumption of 321 clients (Monash Forecasting Archive), for forecasting.

One example per client: the hourly load in kW from 2012 to 2014, 26,304 points. Splits follow the usual forecasting protocol (Chronos, GluonTS): test holds each full series and the forecast target is its last PREDICTION_LENGTH points; train holds the same series with those points removed.

FREQ = 'h'
PREDICTION_LENGTH = 24
SEASONALITY = 24
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/4656140', assets=mappingproxy({'train': DownloadInfo(url='https://zenodo.org/records/4656140/files/electricity_hourly_dataset.zip?download=1', fallbacks=[], checksum='sha256:eff447075dde68dca0105ab7e2851c5637967ae3bb21556fd8b931f196d5968c', filename='electricity_hourly_dataset.zip'), 'test': DownloadInfo(url='https://zenodo.org/records/4656140/files/electricity_hourly_dataset.zip?download=1', fallbacks=[], checksum='sha256:eff447075dde68dca0105ab7e2851c5637967ae3bb21556fd8b931f196d5968c', filename='electricity_hourly_dataset.zip')}), citation='@inproceedings{godahewa2021monash,\n    title={Monash Time Series Forecasting Archive},\n    author={Godahewa, Rakshitha and Bergmeir, Christoph and Webb, Geoffrey I. and Hyndman, Rob J. and Montero-Manso, Pablo},\n    booktitle={Neural Information Processing Systems Track on Datasets and Benchmarks},\n    year={2021}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.epilepsy module

class Epilepsy(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Epilepsy multivariate time series classification dataset from the UEA archive.

275 recordings of a tri-axial accelerometer on the dominant wrist of six healthy participants, sampled at 16 Hz and truncated by the archive to 206 steps. Each recording shows one of four activities: a mimicked epileptic seizure, walking, running or sawing. The archive’s split puts three participants in train (137 recordings) and the other three in test (138). Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 3
SEQUENCE_LENGTH = 206
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=Epilepsy', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/Epilepsy.zip', fallbacks=['https://web.archive.org/web/20250225182932id_/https://timeseriesclassification.com/aeon-toolkit/Epilepsy.zip'], checksum='sha256:8e5ce818a4989e520aa620f3009621692f06c8e8e73d72ee98cc0dbdee7c10c9', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/Epilepsy.zip', fallbacks=['https://web.archive.org/web/20250225182932id_/https://timeseriesclassification.com/aeon-toolkit/Epilepsy.zip'], checksum='sha256:8e5ce818a4989e520aa620f3009621692f06c8e8e73d72ee98cc0dbdee7c10c9', filename=None)}), citation='@article{villar2016generalized,\n    title={Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition},\n    author={Villar, José R. and Vergara, Paula and Menéndez, Manuel and de la Cal, Enrique and González, Víctor M. and Sedano, Javier},\n    journal={International Journal of Neural Systems},\n    volume={26},\n    number={6},\n    pages={1650037},\n    year={2016}\n}\n@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.freefield1010 module

class Freefield1010(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Freefield1010 bird-presence classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/#downloads', assets=mappingproxy({'audio': DownloadInfo(url='https://archive.org/download/ff1010bird/ff1010bird_wav.zip', fallbacks=[], checksum=None, filename='ff1010bird_wav.zip'), 'metadata': DownloadInfo(url='https://ndownloader.figshare.com/files/6035814', fallbacks=[], checksum=None, filename='ff1010bird_metadata.csv')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.fsd_kaggle_2018 module

class FSDKaggle2018(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

FSDKaggle2018 sound classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/2552860', assets=mappingproxy({'train_audio': DownloadInfo(url='https://zenodo.org/record/2552860/files/FSDKaggle2018.audio_train.zip?download=1', fallbacks=['https://zenodo.org/records/2552860/files/FSDKaggle2018.audio_train.zip'], checksum=None, filename='FSDKaggle2018.audio_train.zip'), 'test_audio': DownloadInfo(url='https://zenodo.org/record/2552860/files/FSDKaggle2018.audio_test.zip?download=1', fallbacks=['https://zenodo.org/records/2552860/files/FSDKaggle2018.audio_test.zip'], checksum=None, filename='FSDKaggle2018.audio_test.zip'), 'meta': DownloadInfo(url='https://zenodo.org/record/2552860/files/FSDKaggle2018.meta.zip?download=1', fallbacks=['https://zenodo.org/records/2552860/files/FSDKaggle2018.meta.zip'], checksum=None, filename='FSDKaggle2018.meta.zip')}), citation='@dataset{fonseca2019fsdkaggle2018,\n                         title={FSDKaggle2018},\n                         author={Fonseca, Eduardo and Plakal, Manoj and Font, Frederic and Ellis, Daniel P.W. and Serra, Xavier},\n                         year={2019},\n                         publisher={Zenodo},\n                         doi={10.5281/zenodo.2552860}}', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.groove_MIDI module

class GrooveMIDI(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Groove MIDI dataset with aligned synthesized audio and MIDI files.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://magenta.tensorflow.org/datasets/groove', assets=mappingproxy({'archive': DownloadInfo(url='https://storage.googleapis.com/magentadata/datasets/groove/groove-v1.0.0.zip', fallbacks=[], checksum=None, filename='groove-v1.0.0.zip')}), citation='@inproceedings{groove2019,\n                     author={Jon Gillick and Adam Roberts and Jesse Engel and Douglas Eck and David Bamman},\n                     title={Learning to Groove with Inverse Sequence Transformations},\n                     booktitle={International Conference on Machine Learning (ICML)},\n                     year={2019}}', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.gtzan module

class GTZAN(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

GTZAN music genre classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://marsyas.info/downloads/datasets.html', assets=mappingproxy({'train': DownloadInfo(url='http://opihi.cs.uvic.ca/sound/genres.tar.gz', fallbacks=[], checksum=None, filename='genres.tar.gz')}), citation='@article{tzanetakis2002musical,\n                     title={Musical genre classification of audio signals},\n                     author={Tzanetakis, George and Cook, Perry},\n                     journal={IEEE Transactions on Speech and Audio Processing},\n                     year={2002}}', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.hand_movement_direction module

class HandMovementDirection(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

HandMovementDirection multivariate time series classification dataset from the UEA archive.

MEG of two healthy subjects moving a joystick with the right hand and wrist toward one of four self-chosen targets (BCI Competition IV, data set 3). Each trial covers 0.4 s before to 0.6 s after movement onset, 10 MEG channels above the motor areas at 400 Hz (400 steps), labelled backward, forward, left or right. The archive keeps the competition’s split but merges the two subjects: train holds 160 trials, test 74. Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 10
SEQUENCE_LENGTH = 400
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=HandMovementDirection', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/HandMovementDirection.zip', fallbacks=[], checksum='sha256:953697729f390de68cc36f3912c65bb7f856ee80c5853818ed7aee7d400d27f2', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/HandMovementDirection.zip', fallbacks=[], checksum='sha256:953697729f390de68cc36f3912c65bb7f856ee80c5853818ed7aee7d400d27f2', filename=None)}), citation='@article{tangermann2012review,\n    title={Review of the BCI Competition IV},\n    author={Tangermann, Michael and Müller, Klaus-Robert and Aertsen, Ad and Birbaumer, Niels and Braun, Christoph and Brunner, Clemens and Leeb, Robert and Mehring, Carsten and Miller, Kai J. and Müller-Putz, Gernot R. and Nolte, Guido and Pfurtscheller, Gert and Preissl, Hubert and Schalk, Gerwin and Schlögl, Alois and Vidaurre, Carmen and Waldert, Stephan and Blankertz, Benjamin},\n    journal={Frontiers in Neuroscience},\n    volume={6},\n    pages={55},\n    year={2012}\n}\n@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.high_gamma module

High-Gamma dataset builder stub.

class HighGamma(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

High-Gamma motor-movement EEG dataset (Schirrmeister et al., 2017). Not implemented yet.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://github.com/robintibor/high-gamma-dataset', assets=mappingproxy({}), citation='TBD', license='', checksums=None)
VERSION: Version = Version('0.0.0')

stable_datasets.timeseries.mhealth module

MHEALTH continuous recordings with all channels and sample-level labels.

class MHEALTH(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

One (time, 23-channel) recording per subject in a single ‘train’ split.

CHANNEL_NAMES = ('chest_acc_x', 'chest_acc_y', 'chest_acc_z', 'ecg_1', 'ecg_2', 'ankle_acc_x', 'ankle_acc_y', 'ankle_acc_z', 'ankle_gyro_x', 'ankle_gyro_y', 'ankle_gyro_z', 'ankle_mag_x', 'ankle_mag_y', 'ankle_mag_z', 'arm_acc_x', 'arm_acc_y', 'arm_acc_z', 'arm_gyro_x', 'arm_gyro_y', 'arm_gyro_z', 'arm_mag_x', 'arm_mag_y', 'arm_mag_z')
SAMPLE_RATE = 50
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://archive.ics.uci.edu/dataset/319/mhealth+dataset', assets=mappingproxy({'train': DownloadInfo(url='https://archive.ics.uci.edu/static/public/319/mhealth+dataset.zip', fallbacks=[], checksum='sha256:16ad0ce709f3f00df18f348610d15bce0884b79e2143f57f446493673f02b8e0', filename='MHEALTH.zip')}), citation='@misc{mhealth_2014,\n  author = {Banos, Oresti and Garcia, Rafael and Saez, Alejandro},\n  title = {MHEALTH},\n  year = {2014},\n  publisher = {UCI Machine Learning Repository},\n  doi = {10.24432/C5TW22},\n  url = {https://doi.org/10.24432/C5TW22}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.natops module

class NATOPS(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

NATOPS multivariate time series classification dataset from the UEA archive.

Six aircraft handling signals from the Naval Air Training and Operating Procedures Standardization (NATOPS), recorded by sensors on both hand tips, elbows, wrists and thumbs: x, y and z coordinates of the eight locations (24 channels), 51 steps. The data comes from the AALTD 2016 challenge (Ghouaiel et al., 2017). train and test hold 180 recordings each. Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 24
SEQUENCE_LENGTH = 51
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=NATOPS', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/NATOPS.zip', fallbacks=['https://web.archive.org/web/20250225053625id_/https://timeseriesclassification.com/aeon-toolkit/NATOPS.zip'], checksum='sha256:38e91561d70426b508abf6db253070b1bed00a1c303d70844997ef4d4ab3b7ff', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/NATOPS.zip', fallbacks=['https://web.archive.org/web/20250225053625id_/https://timeseriesclassification.com/aeon-toolkit/NATOPS.zip'], checksum='sha256:38e91561d70426b508abf6db253070b1bed00a1c303d70844997ef4d4ab3b7ff', filename=None)}), citation='@article{ghouaiel2017continuous,\n    title={Continuous pattern detection and recognition in stream - a benchmark for online gesture recognition},\n    author={Ghouaiel, Nehla and Marteau, Pierre-François and Dupont, Marc},\n    journal={International Journal of Applied Pattern Recognition},\n    volume={4},\n    number={2},\n    year={2017}\n}\n@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.picidae module

class Picidae(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Picidae birdsong classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/574438', assets=mappingproxy({'train': DownloadInfo(url='https://zenodo.org/record/574438/files/PicidaeDataset.zip?download=1', fallbacks=['https://zenodo.org/records/574438/files/PicidaeDataset.zip'], checksum=None, filename='PicidaeDataset.zip')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.racket_sports module

class RacketSports(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

RacketSports multivariate time series classification dataset from the UEA archive.

303 recordings from a smart watch worn by university students playing badminton or squash: the x, y and z axes of its gyroscope and accelerometer (6 channels), sampled at 10 Hz for 3 seconds (30 steps). The task is to tell the sport and the stroke: badminton clear or smash, squash forehand or backhand boast. The archive’s split has 151 recordings in train and 152 in test. Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 6
SEQUENCE_LENGTH = 30
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=RacketSports', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/RacketSports.zip', fallbacks=['https://web.archive.org/web/20250224100042id_/https://timeseriesclassification.com/aeon-toolkit/RacketSports.zip'], checksum='sha256:230c10f30f6800297313eb9aa77a9c2c8ba0714ce1364d3356cb3c342f69a90a', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/RacketSports.zip', fallbacks=['https://web.archive.org/web/20250224100042id_/https://timeseriesclassification.com/aeon-toolkit/RacketSports.zip'], checksum='sha256:230c10f30f6800297313eb9aa77a9c2c8ba0714ce1364d3356cb3c342f69a90a', filename=None)}), citation='@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.robot_execution_failures module

Original force/torque windows from UCI Robot Execution Failures.

class RobotExecutionFailures(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Keep the five learning problems separate and preserve all source records.

Each configuration exposes an unsplit train collection: the source has no official evaluation partition or recording/session identifiers. Evaluation splitting belongs to the experiment, not this loader. Packaged executables are never extracted or run.

BUILDER_CONFIGS: list = [BuilderConfig(name='lp1', version=Version('1.0.1'), description=''), BuilderConfig(name='lp2', version=Version('1.0.1'), description=''), BuilderConfig(name='lp3', version=Version('1.0.1'), description=''), BuilderConfig(name='lp4', version=Version('1.0.1'), description=''), BuilderConfig(name='lp5', version=Version('1.0.1'), description='')]
CHANNEL_NAMES = ('Fx', 'Fy', 'Fz', 'Tx', 'Ty', 'Tz')
CLASS_NAMES = {'lp1': ('normal', 'collision', 'obstruction', 'fr_collision'), 'lp2': ('normal', 'back_col', 'front_col', 'right_col', 'left_col'), 'lp3': ('ok', 'moved', 'slightly_moved', 'lost'), 'lp4': ('normal', 'collision', 'obstruction'), 'lp5': ('normal', 'collision_in_tool', 'collision_in_part', 'bottom_obstruction', 'bottom_collision')}
DEFAULT_CONFIG_NAME: str | None = 'lp1'
SEQUENCE_LENGTH = 15
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://archive.ics.uci.edu/dataset/138/robot+execution+failures', assets=mappingproxy({'train': DownloadInfo(url='https://archive.ics.uci.edu/static/public/138/robot+execution+failures.zip', fallbacks=[], checksum='sha256:cdfbe0b260f4a21c5249fe0da4b1baf4041959663a9182253885ee1f41a4568e', filename='robot_execution_failures.zip')}), citation='@misc{robot_execution_failures_138,\n  author = {Lopes, Luis and Camarinha-Matos, Luis},\n  title = {Robot Execution Failures},\n  year = {1998},\n  publisher = {UCI Machine Learning Repository},\n  doi = {10.24432/C5M89N},\n  url = {https://doi.org/10.24432/C5M89N}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.1')

stable_datasets.timeseries.seizures_neonatal module

class SeizuresNeonatal(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Neonatal EEG recordings with expert seizure annotations.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/2547147', assets=mappingproxy({'annotations': DownloadInfo(url='https://zenodo.org/record/2547147/files/annotations_2017.mat?download=1', fallbacks=['https://zenodo.org/records/2547147/files/annotations_2017.mat'], checksum=None, filename='annotations_2017.mat'), 'eeg1': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg1.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg1.edf'], checksum=None, filename='eeg1.edf'), 'eeg2': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg2.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg2.edf'], checksum=None, filename='eeg2.edf'), 'eeg3': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg3.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg3.edf'], checksum=None, filename='eeg3.edf'), 'eeg4': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg4.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg4.edf'], checksum=None, filename='eeg4.edf'), 'eeg5': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg5.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg5.edf'], checksum=None, filename='eeg5.edf'), 'eeg6': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg6.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg6.edf'], checksum=None, filename='eeg6.edf'), 'eeg7': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg7.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg7.edf'], checksum=None, filename='eeg7.edf'), 'eeg8': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg8.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg8.edf'], checksum=None, filename='eeg8.edf'), 'eeg9': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg9.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg9.edf'], checksum=None, filename='eeg9.edf'), 'eeg10': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg10.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg10.edf'], checksum=None, filename='eeg10.edf'), 'eeg11': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg11.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg11.edf'], checksum=None, filename='eeg11.edf'), 'eeg12': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg12.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg12.edf'], checksum=None, filename='eeg12.edf'), 'eeg13': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg13.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg13.edf'], checksum=None, filename='eeg13.edf'), 'eeg14': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg14.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg14.edf'], checksum=None, filename='eeg14.edf'), 'eeg15': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg15.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg15.edf'], checksum=None, filename='eeg15.edf'), 'eeg16': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg16.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg16.edf'], checksum=None, filename='eeg16.edf'), 'eeg17': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg17.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg17.edf'], checksum=None, filename='eeg17.edf'), 'eeg18': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg18.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg18.edf'], checksum=None, filename='eeg18.edf'), 'eeg19': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg19.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg19.edf'], checksum=None, filename='eeg19.edf'), 'eeg20': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg20.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg20.edf'], checksum=None, filename='eeg20.edf'), 'eeg21': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg21.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg21.edf'], checksum=None, filename='eeg21.edf'), 'eeg22': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg22.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg22.edf'], checksum=None, filename='eeg22.edf'), 'eeg23': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg23.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg23.edf'], checksum=None, filename='eeg23.edf'), 'eeg24': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg24.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg24.edf'], checksum=None, filename='eeg24.edf'), 'eeg25': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg25.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg25.edf'], checksum=None, filename='eeg25.edf'), 'eeg26': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg26.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg26.edf'], checksum=None, filename='eeg26.edf'), 'eeg27': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg27.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg27.edf'], checksum=None, filename='eeg27.edf'), 'eeg28': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg28.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg28.edf'], checksum=None, filename='eeg28.edf'), 'eeg29': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg29.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg29.edf'], checksum=None, filename='eeg29.edf'), 'eeg30': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg30.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg30.edf'], checksum=None, filename='eeg30.edf'), 'eeg31': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg31.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg31.edf'], checksum=None, filename='eeg31.edf'), 'eeg32': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg32.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg32.edf'], checksum=None, filename='eeg32.edf'), 'eeg33': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg33.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg33.edf'], checksum=None, filename='eeg33.edf'), 'eeg34': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg34.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg34.edf'], checksum=None, filename='eeg34.edf'), 'eeg35': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg35.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg35.edf'], checksum=None, filename='eeg35.edf'), 'eeg36': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg36.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg36.edf'], checksum=None, filename='eeg36.edf'), 'eeg37': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg37.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg37.edf'], checksum=None, filename='eeg37.edf'), 'eeg38': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg38.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg38.edf'], checksum=None, filename='eeg38.edf'), 'eeg39': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg39.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg39.edf'], checksum=None, filename='eeg39.edf'), 'eeg40': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg40.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg40.edf'], checksum=None, filename='eeg40.edf'), 'eeg41': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg41.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg41.edf'], checksum=None, filename='eeg41.edf'), 'eeg42': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg42.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg42.edf'], checksum=None, filename='eeg42.edf'), 'eeg43': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg43.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg43.edf'], checksum=None, filename='eeg43.edf'), 'eeg44': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg44.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg44.edf'], checksum=None, filename='eeg44.edf'), 'eeg45': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg45.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg45.edf'], checksum=None, filename='eeg45.edf'), 'eeg46': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg46.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg46.edf'], checksum=None, filename='eeg46.edf'), 'eeg47': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg47.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg47.edf'], checksum=None, filename='eeg47.edf'), 'eeg48': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg48.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg48.edf'], checksum=None, filename='eeg48.edf'), 'eeg49': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg49.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg49.edf'], checksum=None, filename='eeg49.edf'), 'eeg50': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg50.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg50.edf'], checksum=None, filename='eeg50.edf'), 'eeg51': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg51.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg51.edf'], checksum=None, filename='eeg51.edf'), 'eeg52': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg52.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg52.edf'], checksum=None, filename='eeg52.edf'), 'eeg53': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg53.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg53.edf'], checksum=None, filename='eeg53.edf'), 'eeg54': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg54.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg54.edf'], checksum=None, filename='eeg54.edf'), 'eeg55': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg55.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg55.edf'], checksum=None, filename='eeg55.edf'), 'eeg56': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg56.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg56.edf'], checksum=None, filename='eeg56.edf'), 'eeg57': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg57.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg57.edf'], checksum=None, filename='eeg57.edf'), 'eeg58': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg58.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg58.edf'], checksum=None, filename='eeg58.edf'), 'eeg59': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg59.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg59.edf'], checksum=None, filename='eeg59.edf'), 'eeg60': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg60.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg60.edf'], checksum=None, filename='eeg60.edf'), 'eeg61': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg61.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg61.edf'], checksum=None, filename='eeg61.edf'), 'eeg62': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg62.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg62.edf'], checksum=None, filename='eeg62.edf'), 'eeg63': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg63.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg63.edf'], checksum=None, filename='eeg63.edf'), 'eeg64': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg64.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg64.edf'], checksum=None, filename='eeg64.edf'), 'eeg65': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg65.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg65.edf'], checksum=None, filename='eeg65.edf'), 'eeg66': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg66.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg66.edf'], checksum=None, filename='eeg66.edf'), 'eeg67': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg67.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg67.edf'], checksum=None, filename='eeg67.edf'), 'eeg68': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg68.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg68.edf'], checksum=None, filename='eeg68.edf'), 'eeg69': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg69.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg69.edf'], checksum=None, filename='eeg69.edf'), 'eeg70': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg70.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg70.edf'], checksum=None, filename='eeg70.edf'), 'eeg71': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg71.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg71.edf'], checksum=None, filename='eeg71.edf'), 'eeg72': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg72.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg72.edf'], checksum=None, filename='eeg72.edf'), 'eeg73': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg73.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg73.edf'], checksum=None, filename='eeg73.edf'), 'eeg74': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg74.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg74.edf'], checksum=None, filename='eeg74.edf'), 'eeg75': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg75.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg75.edf'], checksum=None, filename='eeg75.edf'), 'eeg76': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg76.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg76.edf'], checksum=None, filename='eeg76.edf'), 'eeg77': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg77.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg77.edf'], checksum=None, filename='eeg77.edf'), 'eeg78': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg78.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg78.edf'], checksum=None, filename='eeg78.edf'), 'eeg79': DownloadInfo(url='https://zenodo.org/record/2547147/files/eeg79.edf?download=1', fallbacks=['https://zenodo.org/records/2547147/files/eeg79.edf'], checksum=None, filename='eeg79.edf')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.sonycust module

class SONYCUST(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

SONYC Urban Sound Tagging development dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/3233082', assets=mappingproxy({'audio': DownloadInfo(url='https://zenodo.org/record/3233082/files/audio-dev.tar.gz?download=1', fallbacks=['https://zenodo.org/records/3233082/files/audio-dev.tar.gz'], checksum=None, filename='audio-dev.tar.gz'), 'annotations': DownloadInfo(url='https://zenodo.org/record/3233082/files/annotations-dev.csv?download=1', fallbacks=['https://zenodo.org/records/3233082/files/annotations-dev.csv'], checksum=None, filename='annotations-dev.csv')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.speech_commands module

class SpeechCommands(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Speech Commands keyword classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://ai.googleblog.com/2017/08/launching-speech-commands-dataset.html', assets=mappingproxy({'train': DownloadInfo(url='http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz', fallbacks=[], checksum=None, filename='speech_commands_v0.01.tar.gz')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.uber_tlc module

class UberTLC(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Uber pickups in New York City per taxi zone, January-June 2015, for forecasting.

Built from the raw trip records FiveThirtyEight obtained from the NYC Taxi & Limousine Commission: every pickup is counted in its taxi zone (locationID) and hour or day, with zero for periods without pickups. One example per zone (262 zones), 4,344 hourly or 181 daily counts. Splits follow the usual forecasting protocol (Chronos, GluonTS): test holds each full series and the forecast target is its last prediction_length points; train holds the same series with those points removed.

Configs: hourly and daily; UberTLC.SETTINGS[config] gives (frequency, prediction length, seasonality).

BUILDER_CONFIGS: list = [BuilderConfig(name='hourly', version=Version('1.0.0'), description='Pickups per zone per hour.'), BuilderConfig(name='daily', version=Version('1.0.0'), description='Pickups per zone per day.')]
DEFAULT_CONFIG_NAME: str | None = 'hourly'
SETTINGS = {'daily': ('D', 7, 1), 'hourly': ('h', 24, 24)}

config name -> (frequency, prediction length, seasonality)

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://github.com/fivethirtyeight/uber-tlc-foil-response', assets=mappingproxy({'train': DownloadInfo(url='https://github.com/fivethirtyeight/uber-tlc-foil-response/raw/master/uber-trip-data/uber-raw-data-janjune-15.csv.zip', fallbacks=[], checksum='sha256:b85c7373ce0b0186eb204e14600033033abf0b2ce47cc97b4ca59e1ec6ec8bec', filename='uber-raw-data-janjune-15.csv.zip'), 'test': DownloadInfo(url='https://github.com/fivethirtyeight/uber-tlc-foil-response/raw/master/uber-trip-data/uber-raw-data-janjune-15.csv.zip', fallbacks=[], checksum='sha256:b85c7373ce0b0186eb204e14600033033abf0b2ce47cc97b4ca59e1ec6ec8bec', filename='uber-raw-data-janjune-15.csv.zip')}), citation='@misc{fivethirtyeight2015uber,\n    title={Uber TLC FOIL Response},\n    author={FiveThirtyEight},\n    howpublished={\\url{https://github.com/fivethirtyeight/uber-tlc-foil-response}},\n    year={2015}\n}', license='See dataset homepage (NYC Taxi & Limousine Commission records, released under FOIL).', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.uci_har module

Official UCI HAR inertial-signal windows and subject-disjoint splits.

class UCIHAR(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

UCI HAR windows in (time, channel) order; labels are zero-based.

CHANNEL_NAMES = ('body_acc_x', 'body_acc_y', 'body_acc_z', 'body_gyro_x', 'body_gyro_y', 'body_gyro_z', 'total_acc_x', 'total_acc_y', 'total_acc_z')
SAMPLE_RATE = 50
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://archive.ics.uci.edu/dataset/240/human+activity+recognition+using+smartphones', assets=mappingproxy({'train': DownloadInfo(url='https://archive.ics.uci.edu/static/public/240/human+activity+recognition+using+smartphones.zip', fallbacks=[], checksum='sha256:c00b803081a5c797cd5e4b83700a9810b38d53d9d84e01917e090e1fdbc81031', filename='UCI_HAR.zip'), 'test': DownloadInfo(url='https://archive.ics.uci.edu/static/public/240/human+activity+recognition+using+smartphones.zip', fallbacks=[], checksum='sha256:c00b803081a5c797cd5e4b83700a9810b38d53d9d84e01917e090e1fdbc81031', filename='UCI_HAR.zip')}), citation='@misc{uci_har_2013,\n  author = {Reyes-Ortiz, Jorge and Anguita, Davide and Ghio, Alessandro and Oneto, Luca and Parra, Xavier},\n  title = {Human Activity Recognition Using Smartphones},\n  year = {2013},\n  publisher = {UCI Machine Learning Repository},\n  doi = {10.24432/C54S4K},\n  url = {https://doi.org/10.24432/C54S4K}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.uwave_gesture_library module

class UWaveGestureLibrary(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

UWaveGestureLibrary multivariate time series classification dataset from the UEA archive.

Accelerometer recordings of eight simple gestures from the uWave gesture library (Liu et al., 2009): the x, y and z acceleration of each motion (3 channels), 315 steps. The classes are the gesture numbers 1 to 8 of the uWave paper. train holds 120 recordings, test 320. Each series has shape (SEQUENCE_LENGTH, NUM_CHANNELS).

NUM_CHANNELS = 3
SEQUENCE_LENGTH = 315
SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://timeseriesclassification.com/description.php?Dataset=UWaveGestureLibrary', assets=mappingproxy({'train': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/UWaveGestureLibrary.zip', fallbacks=[], checksum='sha256:c6a771794bb14a815e204307b7d7e85d7a8291ddb16626cb9460aa5a75b6e042', filename=None), 'test': DownloadInfo(url='https://timeseriesclassification.com/aeon-toolkit/UWaveGestureLibrary.zip', fallbacks=[], checksum='sha256:c6a771794bb14a815e204307b7d7e85d7a8291ddb16626cb9460aa5a75b6e042', filename=None)}), citation='@inproceedings{liu2009uwave,\n    title={uWave: Accelerometer-based Personalized Gesture Recognition and its Applications},\n    author={Liu, Jiayang and Wang, Zhen and Zhong, Lin and Wickramasuriya, Jehan and Vasudevan, Venu},\n    booktitle={2009 IEEE International Conference on Pervasive Computing and Communications},\n    pages={1--9},\n    year={2009}\n}\n@article{bagnall2018uea,\n    title={The UEA multivariate time series classification archive, 2018},\n    author={Bagnall, Anthony and Dau, Hoang Anh and Lines, Jason and Flynn, Michael and Large, James and Bostrom, Aaron and Southam, Paul and Keogh, Eamonn},\n    journal={arXiv preprint arXiv:1811.00075},\n    year={2018}\n}', license='CC BY 4.0', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.vocalset module

class VocalSet(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

VocalSet singing voice dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='https://zenodo.org/records/1442513', assets=mappingproxy({'train': DownloadInfo(url='https://zenodo.org/record/1442513/files/VocalSet11.zip?download=1', fallbacks=['https://zenodo.org/records/1442513/files/VocalSet11.zip'], checksum=None, filename='VocalSet11.zip')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

stable_datasets.timeseries.warblr module

class Warblr(*args, split=None, processed_cache_dir=None, download_dir=None, storage_format=None, backend_kwargs=None, decode_video=None, **kwargs)[source]

Bases: BaseDatasetBuilder

Warblr bird-presence classification dataset.

SOURCE: DatasetSource | Mapping = DatasetSource(homepage='http://machine-listening.eecs.qmul.ac.uk/bird-audio-detection-challenge/#downloads', assets=mappingproxy({'audio': DownloadInfo(url='https://archive.org/download/warblrb10k_public/warblrb10k_public_wav.zip', fallbacks=[], checksum=None, filename='warblrb10k_public_wav.zip'), 'metadata': DownloadInfo(url='https://ndownloader.figshare.com/files/6035817', fallbacks=[], checksum=None, filename='warblrb10k_public_metadata.csv')}), citation='See dataset homepage.', license='', checksums=None)
VERSION: Version = Version('1.0.0')

Module contents