todo updates

dataManager/gFunDataset.py

@@ -118,7 +118,6 @@ class gFunDataset:
 
         if self.data_langs is None:
             data_langs = sorted(train_split.geo.unique().tolist())
-            # TODO: if data langs is NOT none then we have a problem where we filter df by langs
         if self.labels is None:
             labels = train_split.category_name.unique().tolist()
 

gfun/generalizedFunnelling.py

@@ -82,7 +82,7 @@ class GeneralizedFunnelling:
         self.aggfunc = aggfunc
         self.load_trained = load_trained
         self.load_first_tier = (
-            True  # TODO: i guess we're always going to load at least the fitst tier
+            True  # TODO: i guess we're always going to load at least the first tier
         )
         self.load_meta = load_meta
         self.dataset_name = dataset_name

gfun/vgfs/multilingualGen.py

@@ -104,10 +104,6 @@ class MultilingualGen(ViewGen):
             pickle.dump(self, f)
         return self
 
-    def __str__(self):
-        _str = f"[Multilingual VGF (m)]\n- embed_dir: {self.embed_dir}\n- langs: {self.langs}\n- n_jobs: {self.n_jobs}\n- cached: {self.cached}\n- sif: {self.sif}\n- probabilistic: {self.probabilistic}\n"
-        return _str
-
 
 def load_MUSEs(langs, l_vocab, dir_path, cached=False):
     dir_path = expanduser(dir_path)

gfun/vgfs/textualTransformerGen.py

@@ -193,7 +193,7 @@ class TextualTransformerGen(ViewGen, TransformerGen):
         )
         trainer.train(
             train_dataloader=tra_dataloader,
-            eval_dataloader=val_dataloader,  # TODO: debug setting
+            eval_dataloader=val_dataloader,
             epochs=self.epochs,
         )
 
@@ -275,10 +275,6 @@ class TextualTransformerGen(ViewGen, TransformerGen):
         else:
             return model_name
 
-    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/vanillaFun.py

@@ -65,8 +65,3 @@ class VanillaFunGen(ViewGen):
         with open(_path, "wb") as f:
             pickle.dump(self, f)
         return self
-
-    def __str__(self):
-        _str = f"[VanillaFunGen (-p)]\n- base learner: {self.learners}\n- n_jobs: {self.n_jobs}\n"
-        # - parameters: {self.first_tier_parameters}
-        return _str

gfun/vgfs/visualTransformerGen.py

@@ -185,9 +185,5 @@ class VisualTransformerGen(ViewGen, TransformerGen):
             pickle.dump(self, f)
         return self
 
-    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()}

gfun/vgfs/wceGen.py

@@ -40,10 +40,6 @@ class WceGen(ViewGen):
             "sif": self.sif,
         }
 
-    def __str__(self):
-        _str = f"[WordClass VGF (w)]\n- sif: {self.sif}\n- n_jobs: {self.n_jobs}\n"
-        return _str
-
     def save_vgf(self, model_id):
         import pickle
         from os.path import join