Parkinsons

class itergp.datasets.uci.Parkinsons(dir='data/uci/parkinsons', overwrite=False)

Bases: UCIDataset

Parkinsons Telemonitoring dataset (5,875 × 21). 1

This dataset is composed of a range of biomedical voice measurements from 42 people with early-stage Parkinson’s disease recruited to a six-month trial of a telemonitoring device for remote symptom progression monitoring. The recordings were automatically captured in the patient’s homes. The original study used a range of linear and nonlinear regression methods to predict the clinician’s Parkinson’s disease symptom score on the UPDRS scale.

Source: https://archive.ics.uci.edu/ml/datasets/parkinsons+telemonitoring

References

1

A Tsanas, MA Little, PE McSharry, LO Ramig (2009) ‘Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests’, IEEE Transactions on Biomedical Engineering

Attributes Summary

URL
input_shape	Input shape of the data.
output_shape	Output shape of the data.
test	Test data.
train	Training data.

Methods Summary

from_disk(dir)
resample(rng_state)	Resample the training and test set from the entire data set.
save([dir, overwrite])	Save dataset to disk.

Attributes Documentation

Parameters

• dir (Optional[str]) –

• overwrite (bool) –

URL = 'https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/'

input_shape

Input shape of the data.

output_shape

Output shape of the data.

test

Test data.

train

Training data.

Methods Documentation

classmethod from_disk(dir)

Parameters

dir (str) –

Return type

Dataset

resample(rng_state)

Resample the training and test set from the entire data set.

Randomly selects new datapoints for the training and test set of the same sizes as the original dataset.

Parameters

rng_state (SeedSequence) – Random number generator state.

Return type

Dataset

save(dir='data', overwrite=False)

Save dataset to disk.

Parameters

• dir (str) – Directory to save data to.

• overwrite – Whether to overwrite existing data.

Return type

None