mirror of
https://github.com/samsledje/D-SCRIPT.git
synced 2026-06-04 15:04:24 +08:00
88 lines
2.2 KiB
Python
88 lines
2.2 KiB
Python
from __future__ import print_function, division
|
|
|
|
import torch
|
|
import torch.utils.data
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import subprocess as sp
|
|
import sys
|
|
import gzip as gz # handles unix/linux gzip files
|
|
|
|
from datetime import datetime
|
|
|
|
|
|
def log(m, file=None, timestamped=True, print_also=False):
|
|
curr_time = f"[{datetime.now().strftime('%Y-%m-%d-%H:%M:%S')}] "
|
|
log_string = f"{curr_time if timestamped else ''}{m}"
|
|
if file is None:
|
|
print(log_string)
|
|
else:
|
|
print(log_string, file=file)
|
|
if print_also:
|
|
print(log_string)
|
|
|
|
|
|
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)))
|
|
|
|
|
|
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]
|
|
|
|
|
|
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)
|