File size: 1,847 Bytes
34393ef | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import os
import sys
sys.path.append(os.path.abspath("../"))
import numpy as np
import pandas as pd
import torch
from torch import nn
import reader
class Kmer_LinReg(nn.Module):
"""
simple kmer model detect motifs
"""
def __init__(self, kmer_size, pad_to):
super().__init__()
self.k = kmer_size
self.input_length = pad_to
# define Conv then replace the parameters
channel_size = 4**kmer_size
self.kmer_conv = nn.Conv1d(4, 1, kmer_size)
self.custom_conv()
self.kmer_binarizer = nn.ReLU()
out_length = self.compute_kmer_outshape()
self.fc_out = nn.Linear(channel_size * out_length, 1)
def compute_kmer_outshape(self):
"""
default stride = 1, pad = 0
"""
dilation = 1
padding = 0
stride = 1
L_in = self.input_length
L_out = 1 + L_in + 2 * padding - dilation * (self.k - 1) - 1
return L_out
def create_kmer(self):
all_kmer = {0:['']}
k = 1
while k <= self.k:
k_mer = [] # 1 ; 4 ; 2: 4**2 ...
for source in all_kmer[k-1]:
k_mer += [source + base for base in ['A','C','G','T']]
assert len(k_mer) == 4**k, f"new kmers {len(k_mer)}, not equal to {4**k}"
all_kmer[k] = k_mer
k += 1
all_kmer.pop(0)
return all_kmer
def custom_conv(self):
# only detect 5-mer is kmersize is 5
# Zhang et al includes shorter kmers in their features
kmers = self.create_kmer()[self.k]
matrix = [reader.one_hot(kmer).T for kmer in kmers]
kernels = np.stack(matrix)
kernels = matrix[0].reshape(1, 4, 3)
self.kmer_conv.weight = nn.Parameter(torch.from_numpy(kernels).long(), requires_grad=False)
|