[MRG] Add SPA method

README.md

@@ -241,4 +241,6 @@ The library is distributed under the 3-Clause BSD license.
 
 [34] Jin, Ying, Wang, Ximei, Long, Mingsheng, Wang, Jianmin. [Minimum Class Confusion for Versatile Domain Adaptation](https://arxiv.org/pdf/1912.03699). ECCV, 2020. 
 
-[35] Zhang, Y., Liu, T., Long, M., & Jordan, M. I. (2019). [Bridging Theory and Algorithm for Domain Adaptation](https://arxiv.org/abs/1904.05801). In Proceedings of the 36th International Conference on Machine Learning, (pp. 7404-7413).
+[35] Zhang, Y., Liu, T., Long, M., & Jordan, M. I. (2019). [Bridging Theory and Algorithm for Domain Adaptation](https://arxiv.org/abs/1904.05801). In Proceedings of the 36th International Conference on Machine Learning, (pp. 7404-7413).
+
+[36] Xiao, Zhiqing, Wang, Haobo, Jin, Ying, Feng, Lei, Chen, Gang, Huang, Fei, Zhao, Junbo.[SPA: A Graph Spectral Alignment Perspective for Domain Adaptation](https://arxiv.org/pdf/2310.17594). In Neurips, 2023.

skada/deep/__init__.py

@@ -20,6 +20,7 @@
 from ._optimal_transport import DeepJDOT, DeepJDOTLoss
 from ._adversarial import DANN, CDAN, MDD, DANNLoss, CDANLoss, MDDLoss, ModifiedCrossEntropyLoss
 from ._class_confusion import MCC, MCCLoss
+from ._graph_alignment import SPA, SPALoss
 from ._baseline import SourceOnly, TargetOnly
 
 from . import losses
@@ -45,6 +46,8 @@
     "ModifiedCrossEntropyLoss",
     "CANLoss",
     "CAN",
+    'SPALoss',
+    'SPA',
     "SourceOnly",
     "TargetOnly",
 ]

skada/deep/_adversarial.py

@@ -53,13 +53,9 @@ def __init__(self, domain_criterion=None):
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
-        y_pred_t,
         domain_pred_s,
         domain_pred_t,
-        features_s,
-        features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         domain_label = torch.zeros(
@@ -183,13 +179,11 @@ def __init__(self, domain_criterion=None):
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
-        y_pred_t,
         domain_pred_s,
         domain_pred_t,
         features_s,
         features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         dtype = torch.float32
@@ -247,7 +241,14 @@ def __init__(
         self.max_features = max_features
         self.random_state = random_state
 
-    def forward(self, X, sample_domain=None, is_fit=False, return_features=False):
+    def forward(
+        self,
+        X,
+        sample_domain=None,
+        sample_idx=None,
+        is_fit=False,
+        return_features=False,
+    ):
         if is_fit:
             # predict
             y_pred = self.base_module_(X)
@@ -279,6 +280,7 @@ def forward(self, X, sample_domain=None, is_fit=False, return_features=False):
                 domain_pred,
                 features,
                 sample_domain,
+                sample_idx,
             )
         else:
             if return_features:
@@ -448,22 +450,20 @@ def __init__(self, gamma=4.0):
 
     def forward(
         self,
-        y_s,
         y_pred_s,
         y_pred_t,
-        disc_pred_s,
-        disc_pred_t,
-        features_s,
-        features_t,
+        domain_pred_s,
+        domain_pred_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         # TODO: handle binary classification
         # Multiclass classification
         pseudo_label_s = torch.argmax(y_pred_s, axis=-1)
         pseudo_label_t = torch.argmax(y_pred_t, axis=-1)
 
-        disc_loss_s = self.disc_criterion_s(disc_pred_s, pseudo_label_s)
-        disc_loss_t = self.disc_criterion_t(disc_pred_t, pseudo_label_t)
+        disc_loss_s = self.disc_criterion_s(domain_pred_s, pseudo_label_s)
+        disc_loss_t = self.disc_criterion_t(domain_pred_t, pseudo_label_t)
 
         # Compute the MDD loss value
         disc_loss = self.gamma * disc_loss_s - disc_loss_t

skada/deep/_baseline.py

@@ -26,13 +26,7 @@ def __init__(
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
-        y_pred_t,
-        domain_pred_s,
-        domain_pred_t,
-        features_s,
-        features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         return 0

skada/deep/_class_confusion.py

@@ -42,13 +42,8 @@ def __init__(self, T=1, eps=1e-7):
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
         y_pred_t,
-        domain_pred_s,
-        domain_pred_t,
-        features_s,
-        features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         loss = mcc_loss(

skada/deep/_divergence.py

@@ -46,13 +46,9 @@ def __init__(
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
-        y_pred_t,
-        domain_pred_s,
-        domain_pred_t,
         features_s,
         features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         loss = deepcoral_loss(features_s, features_t, self.assume_centered)
@@ -132,13 +128,9 @@ def __init__(self, sigmas=None, eps=1e-7):
 
     def forward(
         self,
-        y_s,
-        y_pred_s,
-        y_pred_t,
-        domain_pred_s,
-        domain_pred_t,
         features_s,
         features_t,
+        **kwargs,
     ):
         """Compute the domain adaptation loss"""
         loss = dan_loss(features_s, features_t, sigmas=self.sigmas, eps=self.eps)
@@ -235,12 +227,9 @@ def __init__(
     def forward(
         self,
         y_s,
-        y_pred_s,
-        y_pred_t,
-        domain_pred_s,
-        domain_pred_t,
         features_s,
         features_t,
+        **kwargs,
     ):
         loss = cdd_loss(
             y_s,