format

src/learn_to_pick/byom/pytorch_feature_embedder.py

@@ -2,6 +2,7 @@
 import torch
 from learn_to_pick import PickBestFeaturizer
 
+
 class PyTorchFeatureEmbedder:
     def __init__(self, auto_embed=False, model=None, *args, **kwargs):
         if model is None:
@@ -10,34 +11,39 @@ def __init__(self, auto_embed=False, model=None, *args, **kwargs):
         self.model = model
         self.featurizer = PickBestFeaturizer(auto_embed=auto_embed)
 
-
     def encode(self, stuff):
         embeddings = self.model.encode(stuff, convert_to_tensor=True)
         normalized = torch.nn.functional.normalize(embeddings)
         return normalized
 
-
     def convert_features_to_text(self, features):
         def process_feature(feature):
             if isinstance(feature, dict):
-                return " ".join([f"{k}_{process_feature(v)}" for k, v in feature.items()])
+                return " ".join(
+                    [f"{k}_{process_feature(v)}" for k, v in feature.items()]
+                )
             elif isinstance(feature, list):
                 return " ".join([process_feature(elem) for elem in feature])
             else:
                 return str(feature)
 
         return process_feature(features)
 
-
     def format(self, event):
         # TODO: handle dense
-        context_featurized, actions_featurized, selected = self.featurizer.featurize(event)
+        context_featurized, actions_featurized, selected = self.featurizer.featurize(
+            event
+        )
 
-        context_sparse = self.encode([self.convert_features_to_text(context_featurized.sparse)])
+        context_sparse = self.encode(
+            [self.convert_features_to_text(context_featurized.sparse)]
+        )
 
         actions_sparse = []
         for action_featurized in actions_featurized:
-            actions_sparse.append(self.convert_features_to_text(action_featurized.sparse))
+            actions_sparse.append(
+                self.convert_features_to_text(action_featurized.sparse)
+            )
         actions_sparse = self.encode(actions_sparse).unsqueeze(0)
 
         if selected.score is not None:

src/learn_to_pick/byom/pytorch_policy.py

@@ -48,7 +48,6 @@ def predict(self, event):
         # print(f"returning: {r}")
         return r
 
-
     def learn(self, event):
         R, X, A = self.feature_embedder.format(event)
         # print(f"R: {R}")