Merge pull request #864 from marrlab/causalIRL

first version causal irl

domainlab/algos/trainers/train_causIRL.py

@@ -0,0 +1,77 @@
+"""
+Alex, Xudong
+"""
+import numpy as np
+import torch
+from domainlab.algos.trainers.train_basic import TrainerBasic
+
+
+class TrainerCausalIRL(TrainerBasic):
+    """
+    causal matching
+    """
+    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)
+
+    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
+
+    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
+
+    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

domainlab/algos/trainers/zoo_trainer.py

@@ -4,11 +4,13 @@
 from domainlab.algos.trainers.train_basic import TrainerBasic
 from domainlab.algos.trainers.train_ema import TrainerMA
 from domainlab.algos.trainers.train_dial import TrainerDIAL
-from domainlab.algos.trainers.train_hyper_scheduler import TrainerHyperScheduler
+from domainlab.algos.trainers.train_hyper_scheduler \
+    import TrainerHyperScheduler
 from domainlab.algos.trainers.train_matchdg import TrainerMatchDG
 from domainlab.algos.trainers.train_mldg import TrainerMLDG
 from domainlab.algos.trainers.train_fishr import TrainerFishr
 from domainlab.algos.trainers.train_irm import TrainerIRM
+from domainlab.algos.trainers.train_causIRL import TrainerCausalIRL
 
 
 class TrainerChainNodeGetter(object):
@@ -54,6 +56,7 @@ def __call__(self, lst_candidates=None, default=None, lst_excludes=None):
         chain = TrainerFishr(chain)
         chain = TrainerIRM(chain)
         chain = TrainerHyperScheduler(chain)
+        chain = TrainerCausalIRL(chain)
         node = chain.handle(self.request)
         head = node
         while self._list_str_trainer:

tests/test_causal_irl.py

@@ -0,0 +1,13 @@
+"""
+ end-end test
+"""
+from tests.utils_test import utils_test_algo
+
+
+def test_causal_irl():
+    """
+    causal irl
+    """
+    args = "--te_d 0 --tr_d 3 7 --bs=32 --debug --task=mnistcolor10 \
+        --model=erm --nname=conv_bn_pool_2 --trainer=causalirl"
+    utils_test_algo(args)