KEGGUndir

class itergp.datasets.uci.KEGGUndir(dir='data/uci/keggu', overwrite=False)

Bases: UCIDataset

KEGG Metabolic pathways (Undirected) dataset (63,608 × 26).

KEGG Metabolic pathways modelled as a graph. A variety of network features were computed using Cytoscape. 1

Source: https://archive.ics.uci.edu/ml/datasets/KEGG+Metabolic+Reaction+Network+(Undirected)

References

1

Shannon,P., Markiel,A., Ozier,O., Baliga,N.S., Wang,J.T.,Ramage,D., Amin,N., Schwikowski,B. and Ideker,T. (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13

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/00221/'

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