from __future__ import print_function,division
import torch
import torch.utils.data
from torch.nn.utils.rnn import PackedSequence, pack_padded_sequence, pad_packed_sequence
from .fasta import parse
import numpy as np
import pandas as pd
import subprocess as sp
import sys
import gzip as gz
[docs]def log(m,file=None):
if file is None:
print(m)
else:
print(m,file=file)
[docs]def RBF(D, sigma=None):
"""
Convert distance matrix into similarity matrix using Radial Basis Function (RBF) Kernel.
:math:`RBF(x,x') = \\exp{\\frac{-(x - x')^{2}}{2\\sigma^{2}}}`
:param D: Distance matrix
:type D: np.ndarray
:param sigma: Bandwith of RBF Kernel [default: :math:`\\sqrt{\\text{max}(D)}`]
:type sigma: float
:return: Similarity matrix
:rtype: np.ndarray
"""
sigma = sigma or np.sqrt(np.max(D))
return np.exp(-1 * (np.square(D) / (2 * sigma**2)))
[docs]class PairedDataset(torch.utils.data.Dataset):
"""
Dataset to be used by the PyTorch data loader for pairs of sequences and their labels.
:param X0: List of first item in the pair
:param X1: List of second item in the pair
:param Y: List of labels
"""
def __init__(self, X0, X1, Y):
self.X0 = X0
self.X1 = X1
self.Y = Y
assert len(X0) == len(X1), "X0: "+str(len(X0))+" X1: "+str(len(X1))+" Y: "+str(len(Y))
assert len(X0) == len(Y), "X0: "+str(len(X0))+" X1: "+str(len(X1))+" Y: "+str(len(Y))
def __len__(self):
return len(self.X0)
def __getitem__(self, i):
return self.X0[i], self.X1[i], self.Y[i]
[docs]def collate_paired_sequences(args):
"""
Collate function for PyTorch data loader.
"""
x0 = [a[0] for a in args]
x1 = [a[1] for a in args]
y = [a[2] for a in args]
return x0, x1, torch.stack(y, 0)