Source code for immuneML.ml_methods.pytorch_implementations.PyTorchReceptorCNN

import numpy as np
import torch
from torch import nn
from torch.nn.functional import relu

from immuneML.environment.EnvironmentSettings import EnvironmentSettings
from immuneML.environment.SequenceType import SequenceType

[docs]class PyTorchReceptorCNN(nn.Module): """ This class implements the ReceptorCNN using PyTorch. This is one specific implementation of the architecture proposed in :py:obj:`~immuneML.ml_methods.ReceptorCNN.ReceptorCNN`. """ def __init__(self, kernel_count: int, kernel_size, positional_channels: int, sequence_type: SequenceType, background_probabilities, chain_names): super(PyTorchReceptorCNN, self).__init__() self.background_probabilities = background_probabilities self.threshold = 0.1 self.pseudocount = 0.05 self.in_channels = len(EnvironmentSettings.get_sequence_alphabet(sequence_type)) + positional_channels self.positional_channels = positional_channels self.max_information_gain = self.get_max_information_gain() self.chain_names = chain_names self.conv_chain_1 = [f"chain_1_kernel_{size}" for size in kernel_size] self.conv_chain_2 = [f"chain_2_kernel_{size}" for size in kernel_size] for size in kernel_size: # chain 1 setattr(self, f"chain_1_kernel_{size}", nn.Conv1d(in_channels=self.in_channels, out_channels=kernel_count, kernel_size=size, bias=True)) getattr(self, f"chain_1_kernel_{size}") \ normal_(0.0, np.sqrt(1 /, f"chain_1_kernel_{size}").weight.shape))) # chain 2 setattr(self, f"chain_2_kernel_{size}", nn.Conv1d(in_channels=self.in_channels, out_channels=kernel_count, kernel_size=size, bias=True)) getattr(self, f"chain_2_kernel_{size}") \ normal_(0.0, np.sqrt(1 /, f"chain_2_kernel_{size}").weight.shape))) self.fully_connected = nn.Linear(in_features=kernel_count * len(kernel_size) * 2, out_features=1, bias=True), np.sqrt(1 /
[docs] def get_max_information_gain(self): """ Information gain corresponds to Kullback-Leibler divergence between the observed probability p of an option (e.g. amino acid) ond null (or background) probability q of the option: .. math:: KL(p||q) = \\sum_n p_n \\, log_2 \\, \\frac{p_n}{q_n} = \\sum_n p_n \\, log_2 \\, p_n - \\sum_n p_n \\, log_2 \\, q_n = \\sum_n p_n \\, log_2 \\, p_n - log_2 \\, q_n log_2 \\, q_n < 0 \\, \\, \\, (1) \\sum_n p_n \\, log_2 \\, p_n < 0 \\, \\, \\, (2) (1) \\wedge (2) \\Rightarrow max(KL(p||q)) = - log_2 \\, q_n Returns: max information gain given background probabilities """ if all(self.background_probabilities[i] == self.background_probabilities[0] for i in range(len(self.background_probabilities))): return - np.log2(self.background_probabilities[0]) else: raise NotImplementedError("ReceptorCNN: non-uniform background probabilities are currently not supported.")
[docs] def forward(self, x): """ Implements the forward pass through the network by applying kernels to the one-hot encoded receptors, followed by ReLU activation and max pooling. The obtained output is then concatenated to get the receptor representation. A fully-connected layer is then applied to the representation to predict the class assignment. Args: x: input data consisting of one-hot encoded immune receptors with optional positional information Returns: predictions of class assignment """ # creates batch_size x kernel_count representation of chain 1 and chain 2 by applying kernels, followed by relu and global max pooling chain_1 =[torch.max(relu(conv_kernel(x[:, 0])), dim=2)[0] for conv_kernel in [getattr(self, name) for name in self.conv_chain_1]], dim=1) chain_2 =[torch.max(relu(conv_kernel(x[:, 0])), dim=2)[0] for conv_kernel in [getattr(self, name) for name in self.conv_chain_2]], dim=1) # creates a single representation of the receptor from chain representations receptor =[chain_1, chain_2], dim=1).squeeze() # predict the class of the receptor through the final fully-connected layer based on the inferred representations predictions = self.fully_connected(receptor).squeeze() return predictions
[docs] def rescale_weights_for_IGM(self): """Rescales the weights in the kernels to represent information gain matrices.""" for name in self.conv_chain_1 + self.conv_chain_2: value = getattr(self, name) value.weight = self._rescale_chain(value.weight)
def _rescale_chain(self, weight_parameter): # dimension for the content (without positional information): dim = self.in_channels - self.positional_channels # enforce non-negativity constraint weight_chain = relu(weight_parameter[:, :dim, :]) # add pseudocount for positions where the total sum is below threshold weight_chain[torch.sum(weight_chain, dim=1, keepdim=True).expand_as(weight_chain) < self.threshold] += self.pseudocount # rescale chain weights to represent IGM weight_chain = weight_chain / torch.sum(weight_chain, dim=1, keepdim=True).expand_as(weight_chain) * self.max_information_gain # append positional kernel values to rescaled weights and convert back to parameter weight_parameter = nn.Parameter([weight_chain, weight_parameter[:, dim:, :]], dim=1)) return weight_parameter