JonnyTran

🏂    63km ████████████▊░░░░░░░
🚶‍♂️    21km ████▏░░░░░░░░░░░░░░░
‍🏃‍♂️    12km ██▍░░░░░░░░░░░░░░░░░
🥾     2km ▏░░░░░░░░░░░░░░░░░░░
🧗     0km ░░░░░░░░░░░░░░░░░░░░
🕺🏻     0km ░░░░░░░░░░░░░░░░░░░░
🚴‍♂️     0km ░░░░░░░░░░░░░░░░░░░░
🤸🏻     0km ░░░░░░░░░░░░░░░░░░░░
      96km total

JonnyTran

from typing import Dict, Tuple, Optional, Union, List
from torch_geometric.data import HeteroData
from torch_sparse import SparseTensor
from dgl.heterograph import DGLBlock, DGLHeteroGraph
from dgl._deprecate.graph import DGLGraph
from argparse import Namespace
from collections import OrderedDict
from collections.abc import MutableMapping
import torch

JonnyTran

########### Data Loaders #############
def collate_fn(batch):
    protein_seqs_all, physical, genetic, correlation, y_all, idx_all = [], [], [] ,[], [], []
    for X, y, idx in batch:
        protein_seqs_all.append(torch.tensor(X["Protein_seqs"]))
        physical.append(torch.tensor(X["Protein-Protein-physical"]))
        genetic.append(torch.tensor(X["Protein-Protein-genetic"]))
        correlation.append(torch.tensor(X["Protein-Protein-correlation"]))
        y_all.append(torch.tensor(y))
        idx_all.append(torch.tensor(idx))

JonnyTran

class OnlineTripletLoss(tf.keras.layers.Layer):
    def __init__(self, directed_margin=0.2, undirected_margin=0.1, undirected_weight=1.0, **kwargs):
        super(OnlineTripletLoss, self).__init__(**kwargs)
        self.output_dim = ()
        self.directed_margin = directed_margin
        self.undirected_margin = undirected_margin
        self.undirected_weight = undirected_weight

    def build(self, input_shape):
        assert isinstance(input_shape, list)