Multi-device DAGMA Training and No-val-set Easy training

contextualized/dags/losses.py

@@ -4,7 +4,7 @@
 
 
 def dag_loss_dagma_indiv(w, s=1):
-    M = s * torch.eye(w.shape[-1]) - w * w
+    M = s * torch.eye(w.shape[-1]).to(w.device) - w * w
     return w.shape[-1] * np.log(s) - torch.slogdet(M)[1]
 
 
@@ -18,7 +18,7 @@ def dag_loss_dagma(W, s=1, alpha=0.0, **kwargs):
 
 def dag_loss_poly_indiv(w):
     d = w.shape[-1]
-    return torch.trace((torch.eye(d) + (1 / d) * torch.matmul(w, w))^d) - d
+    return torch.trace((torch.eye(d).to(w.device) + (1 / d) * torch.matmul(w, w)) ** d) - d
 
 
 def dag_loss_poly(W, **kwargs):

contextualized/dags/tests.py

@@ -9,7 +9,7 @@
 from pytorch_lightning.callbacks import LearningRateFinder
 
 
-from contextualized.dags.lightning_modules import NOTMAD
+from contextualized.dags.lightning_modules import NOTMAD, DEFAULT_SS_PARAMS, DEFAULT_ARCH_PARAMS
 from contextualized.dags import graph_utils
 from contextualized.dags.trainers import GraphTrainer
 from contextualized.dags.losses import mse_loss as mse
@@ -37,26 +37,21 @@ def _train(self, model_args, n_epochs):
         model = NOTMAD(
             self.C.shape[-1],
             self.X.shape[-1],
-            archetype_params={
+            archetype_loss_params={
                 "l1": 0.0,
                 "dag": model_args.get(
                     "dag",
-                    {
-                        "loss_type": "NOTEARS",
-                        "params": {
-                            "alpha": 1e-1,
-                            "rho": 1e-2,
-                            "h_old": 0.0,
-                            "tol": 0.25,
-                            "use_dynamic_alpha_rho": True,
-                        },
-                    },
+                    DEFAULT_ARCH_PARAMS["dag"],
                 ),
                 "init_mat": INIT_MAT,
                 "num_factors": model_args.get("num_factors", 0),
                 "factor_mat_l1": 0.0,
                 "num_archetypes": model_args.get("num_archetypes", k),
             },
+            sample_specific_loss_params= {
+                "l1": 0.0,
+                "dag": DEFAULT_SS_PARAMS["dag"],
+            }
         )
         dataloader = model.dataloader(self.C, self.X, batch_size=1, num_workers=0)
         trainer = GraphTrainer(
@@ -181,26 +176,21 @@ def _train(self, model_args, n_epochs):
         model = NOTMAD(
             self.C.shape[-1],
             self.X.shape[-1],
-            archetype_params={
+            archetype_loss_params={
                 "l1": 0.0,
                 "dag": model_args.get(
                     "dag",
-                    {
-                        "loss_type": "NOTEARS",
-                        "params": {
-                            "alpha": 1e-1,
-                            "rho": 1e-2,
-                            "h_old": 0.0,
-                            "tol": 0.25,
-                            "use_dynamic_alpha_rho": True,
-                        },
-                    },
+                    DEFAULT_ARCH_PARAMS["dag"],
                 ),
                 "init_mat": INIT_MAT,
                 "num_factors": model_args.get("num_factors", 0),
                 "factor_mat_l1": 0.0,
                 "num_archetypes": model_args.get("num_archetypes", k),
             },
+            sample_specific_loss_params= {
+                "l1": 0.0,
+                "dag": DEFAULT_SS_PARAMS["dag"],
+            }
         )
         train_dataloader = model.dataloader(
             self.C_train, self.X_train, batch_size=1, num_workers=0

contextualized/easy/wrappers/SKLearnWrapper.py

@@ -335,7 +335,7 @@ def _split_train_data(self, C, X, Y=None, Y_required=False, **kwargs):
             else:
                 print("X_val not provided, not using the provided C_val.")
         if "val_split" in kwargs:
-            if 0 < kwargs["val_split"] < 1:
+            if 0 <= kwargs["val_split"] < 1:
                 val_split = kwargs["val_split"]
             else:
                 print(
@@ -346,15 +346,23 @@ def _split_train_data(self, C, X, Y=None, Y_required=False, **kwargs):
         else:
             val_split = self.default_val_split
         if Y is None:
-            C_train, C_val, X_train, X_val = train_test_split(
-                C, X, test_size=val_split, shuffle=True
-            )
+            if val_split > 0:
+                C_train, C_val, X_train, X_val = train_test_split(
+                    C, X, test_size=val_split, shuffle=True
+                )
+            else:
+                C_train, X_train = C, X
+                C_val, X_val = C, X
             train_data = [C_train, X_train]
             val_data = [C_val, X_val]
         else:
-            C_train, C_val, X_train, X_val, Y_train, Y_val = train_test_split(
-                C, X, Y, test_size=val_split, shuffle=True
-            )
+            if val_split > 0:
+                C_train, C_val, X_train, X_val, Y_train, Y_val = train_test_split(
+                    C, X, Y, test_size=val_split, shuffle=True
+                )
+            else:
+                C_train, X_train, Y_train = C, X, Y
+                C_val, X_val, Y_val = C, X, Y
             train_data = [C_train, X_train, Y_train]
             val_data = [C_val, X_val, Y_val]
         return train_data, val_data

pyproject.toml

@@ -26,7 +26,7 @@ keywords = [
 ]
 dependencies = [
     'lightning>=2.0.0',
-    'torch>=2.0.0,<2.2.0',
+    'torch>=2.0.0',
     'torchvision>=0.8.0',
     'numpy>=1.19.0',
     'pandas>=2.0.0',