"""
Alex, Xudong
"""
import numpy as np
import torch
from domainlab.algos.trainers.train_basic import TrainerBasic
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class TrainerCausalIRL(TrainerBasic):
"""
causal matching
"""
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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)
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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
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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
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def tr_batch(self, tensor_x, tensor_y, tensor_d, others, ind_batch, epoch):
"""
optimize neural network one step upon a mini-batch of data
"""
self.before_batch(epoch, ind_batch)
tensor_x, tensor_y, tensor_d = (
tensor_x.to(self.device),
tensor_y.to(self.device),
tensor_d.to(self.device),
)
self.optimizer.zero_grad()
features = self.get_model().extract_semantic_feat(tensor_x)
pos_batch_break = np.random.randint(0, tensor_x.shape[0])
first = features[:pos_batch_break]
second = features[pos_batch_break:]
if len(first) > 1 and len(second) > 1:
penalty = torch.nan_to_num(self.mmd(first, second))
else:
penalty = torch.tensor(0)
loss = self.cal_loss(tensor_x, tensor_y, tensor_d, others)
loss = loss + penalty
loss.backward()
self.optimizer.step()
self.after_batch(epoch, ind_batch)
self.counter_batch += 1