import os
import numpy as np
from kgcnn.data.datasets.GraphTUDataset2020 import GraphTUDataset2020
from kgcnn.molecule.encoder import OneHotEncoder
[docs]class PROTEINSDataset(GraphTUDataset2020):
r"""Store and process PROTEINS dataset from `TUDatasets <https://chrsmrrs.github.io/datasets/>`__ .
In `Papers with Code <https://paperswithcode.com/dataset/proteins>`__ :
PROTEINS is a dataset of proteins that are classified as enzymes or non-enzymes.
Nodes represent the amino acids and two nodes are connected by an edge if they are less than 6 Angstroms apart.
References:
(1) K. M. Borgwardt, C. S. Ong, S. Schoenauer, S. V. N. Vishwanathan, A. J. Smola, and H. P. Kriegel.
Protein function prediction via graph kernels. Bioinformatics, 21(Suppl 1):i47–i56, Jun 2005.
(2) P. D. Dobson and A. J. Doig. Distinguishing enzyme structures from non-enzymes without alignments.
J. Mol. Biol., 330(4):771–783, Jul 2003.
"""
[docs] def __init__(self, reload=False, verbose: int = 10):
"""Initialize MUTAG dataset.
Args:
reload (bool): Whether to reload the data and make new dataset. Default is False.
verbose (int): Print progress or info for processing where 60=silent. Default is 10.
"""
# Use default base class init()
# We set dataset_name to None since all flags are defined by hand in subclass definition.
super(PROTEINSDataset, self).__init__(dataset_name="PROTEINS", reload=reload, verbose=verbose)
[docs] def read_in_memory(self):
r"""Load PROTEINS Dataset into memory and already split into items with further cleaning and
processing.
"""
super(PROTEINSDataset, self).read_in_memory()
# One-hot encoders
ohe = OneHotEncoder(
[-538, -345, -344, -134, -125, -96, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 41, 42, 47, 61, 63, 73, 74, 75,
82, 104, 353, 355, 360, 558, 797, 798], add_unknown=False)
ohe2 = OneHotEncoder([0, 1, 2], add_unknown=False)
ohe3 = OneHotEncoder([i for i in range(0, 17)], add_unknown=False)
graph_labels = self.obtain_property("graph_labels")
node_attributes = self.obtain_property("node_attributes")
node_labels = self.obtain_property("node_labels")
node_degree = self.obtain_property("node_degree")
self.assign_property("graph_labels", [x - 1 for x in graph_labels])
self.assign_property("node_attributes", [np.array([ohe(int(y)) for y in x]) for x in node_attributes])
self.assign_property("node_labels", [np.array([ohe2(int(y)) for y in x]) for x in node_labels])
self.assign_property("node_degree", [np.array([ohe3(int(y)) for y in x]) for x in node_degree])
self.assign_property("graph_size", [len(x) if x is not None else None for x in node_attributes])
return self
# ds = PROTEINSDataset()