Source code for domainlab.algos.trainers.mmd_base

"""
Alexej, Xudong
"""
import torch
from domainlab.algos.trainers.train_basic import TrainerBasic


[docs] class TrainerMMDBase(TrainerBasic): """ causal matching """
[docs] def my_cdist(self, x1, x2): """ distance for Gaussian """ # along the last dimension x1_norm = x1.pow(2).sum(dim=-1, keepdim=True) x2_norm = x2.pow(2).sum(dim=-1, keepdim=True) # x_2_norm is [batchsize, 1] # matrix multiplication (2nd, 3rd) and addition to first argument # X1[batchsize, dimfeat] * X2[dimfeat, batchsize) # alpha: Scaling factor for the matrix product (default: 1) # x2_norm.transpose(-2, -1) is row vector # x_1_norm is column vector res = torch.addmm(x2_norm.transpose(-2, -1), x1, x2.transpose(-2, -1), alpha=-2).add_(x1_norm) return res.clamp_min_(1e-30)
[docs] def gaussian_kernel(self, x, y): """ kernel for MMD """ gamma = [0.001, 0.01, 0.1, 1, 10, 100, 1000] dist = self.my_cdist(x, y) tensor = torch.zeros_like(dist) for g in gamma: tensor.add_(torch.exp(dist.mul(-g))) return tensor
[docs] def mmd(self, x, y): """ maximum mean discrepancy """ kxx = self.gaussian_kernel(x, x).mean() kyy = self.gaussian_kernel(y, y).mean() kxy = self.gaussian_kernel(x, y).mean() return kxx + kyy - 2 * kxy