getter for gFun and VGFs config

gfun/generalizedFunnelling.py

@@ -309,15 +309,21 @@ class GeneralizedFunnelling:
         return aggregated
 
     def get_config(self):
-        print("\n")
+        c = {}
-        print("-" * 50)
-        print("[GeneralizedFunnelling config]")
-        print(f"- model trained on langs: {self.langs}")
-        print("-- View Generating Functions configurations:\n")
 
         for vgf in self.first_tier_learners:
-            print(vgf)
+            vgf_config = vgf.get_config()
-        print("-" * 50)
+            c.update(vgf_config)
+
+        gfun_config = {
+            "id": self._model_id,
+            "aggfunc": self.aggfunc,
+            "optimc": self.optimc,
+            "dataset": self.dataset_name,
+        }
+
+        c["gFun"] = gfun_config
+        return c
 
     def save(self, save_first_tier=True, save_meta=True):
         print(f"- Saving GeneralizedFunnelling model with id: {self._model_id}")

gfun/vgfs/textualTransformerGen.py

@@ -277,3 +277,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
     def __str__(self):
         str = f"[Transformer VGF (t)]\n- model_name: {self.model_name}\n- max_length: {self.max_length}\n- batch_size: {self.batch_size}\n- batch_size_eval: {self.batch_size_eval}\n- lr: {self.lr}\n- epochs: {self.epochs}\n- device: {self.device}\n- print_steps: {self.print_steps}\n- evaluate_step: {self.evaluate_step}\n- patience: {self.patience}\n- probabilistic: {self.probabilistic}\n"
         return str
+
+    def get_config(self):
+        c = super().get_config()
+        return {"textual_trf": c}

gfun/vgfs/transformerGen.py

@@ -94,3 +94,21 @@ class TransformerGen:
             val_lY[lang] = val_Y
 
         return tr_lX, tr_lY, val_lX, val_lY
+
+    def get_config(self):
+        return {
+            "model_name": self.model_name,
+            "dataset_name": self.dataset_name,
+            "epochs": self.epochs,
+            "lr": self.lr,
+            "batch_size": self.batch_size,
+            "batch_size_eval": self.batch_size_eval,
+            "max_length": self.max_length,
+            "print_steps": self.print_steps,
+            "device": self.device,
+            "probabilistic": self.probabilistic,
+            "n_jobs": self.n_jobs,
+            "evaluate_step": self.evaluate_step,
+            "verbose": self.verbose,
+            "patience": self.patience,
+        }

gfun/vgfs/visualTransformerGen.py

@@ -186,3 +186,6 @@ class VisualTransformerGen(ViewGen, TransformerGen):
     def __str__(self):
         str = f"[Visual Transformer VGF (v)]\n- model_name: {self.model_name}\n- batch_size: {self.batch_size}\n- batch_size_eval: {self.batch_size_eval}\n- lr: {self.lr}\n- epochs: {self.epochs}\n- device: {self.device}\n- print_steps: {self.print_steps}\n- evaluate_step: {self.evaluate_step}\n- patience: {self.patience}\n- probabilistic: {self.probabilistic}\n"
         return str
+
+    def get_config(self):
+        return {"visual_trf": super().get_config()}

main.py

@@ -13,13 +13,10 @@ from gfun.generalizedFunnelling import GeneralizedFunnelling
 """
 TODO:
     - Transformers VGFs:
-        - save/load for MT5ForSqeuenceClassification
         - freeze params method
-        - log on step rather than epoch?
     - General:
         [!] zero-shot setup
         - CLS dataset is loading only "books" domain data
-        - log on wandb also the other VGF results + final results
         - documents should be trimmed to the same length (for SVMs we are using way too long tokens)
     - Attention Aggregator:
         - experiment with weight init of Attention-aggregator
@@ -106,9 +103,10 @@ def main(args):
         n_jobs=args.n_jobs,
     )
 
-    wandb.init(
+    config = gfun.get_config()
-        project="gfun", name=f"gFun-{get_config_name(args)}"
+
-    )  # TODO: Add config to log
+    wandb.init(project="gfun", name=f"gFun-{get_config_name(args)}", config=config)
+
     gfun.fit(lX, lY)
 
     if args.load_trained is None and not args.nosave: