raw -> default_ft

src/learn_to_pick/base.py

@@ -497,10 +497,10 @@ def _embed_string_type(
  result[namespace] = DenseFeatures(model.encode(item.value))
  if item.keep:
  keep_str = item.value.replace(" ", "_")
- result[namespace] = {"raw": re.sub(r"[\t\n\r\f\v]+", " ", keep_str)}
+ result[namespace] = {"default_ft": re.sub(r"[\t\n\r\f\v]+", " ", keep_str)}
  elif isinstance(item, str):
  encoded = item.replace(" ", "_")
- result[namespace] = {"raw": re.sub(r"[\t\n\r\f\v]+", " ", encoded)}
+ result[namespace] = {"default_ft": re.sub(r"[\t\n\r\f\v]+", " ", encoded)}
  else:
  raise ValueError(f"Unsupported type {type(item)} for embedding")
 

src/learn_to_pick/pick_best.py

@@ -123,14 +123,14 @@ def __init__(
  def _dotproducts(self, context, actions):
  _context_dense = base.Featurized()
  for ns in context.sparse.keys():
- if "raw" in context.sparse[ns]:
- _context_dense[ns] = self.model.encode(context.sparse[ns]["raw"])
+ if "default_ft" in context.sparse[ns]:
+ _context_dense[ns] = self.model.encode(context.sparse[ns]["default_ft"])
 
  _actions_dense = [base.Featurized() for _ in range(len(actions))]
  for _action, action in zip(_actions_dense, actions):
  for ns in action.sparse.keys():
- if "raw" in action.sparse[ns]:
- _action[ns] = self.model.encode(action.sparse[ns]["raw"])
+ if "default_ft" in action.sparse[ns]:
+ _action[ns] = self.model.encode(action.sparse[ns]["default_ft"])
 
  context_names = list(_context_dense.dense.keys())
  context_matrix = np.stack(list(_context_dense.dense.values()))
@@ -146,8 +146,8 @@ def _dotproducts(self, context, actions):
  def _generic_namespace(self, featurized):
  result = base.SparseFeatures()
  for ns in featurized.sparse.keys():
- if "raw" in featurized.sparse[ns]:
- result[ns] = featurized.sparse[ns]["raw"]
+ if "default_ft" in featurized.sparse[ns]:
+ result[ns] = featurized.sparse[ns]["default_ft"]
  return result
 
  def _generic_namespaces(self, context, actions):

tests/unit_tests/test_pick_best_call.py

@@ -169,10 +169,10 @@ def test_everything_embedded() -> None:
 
  expected = "\n".join(
  [
- f"shared |User_dense {encoded_ctx_str_1} |User_sparse raw:={ctx_str_1}",
- f"|action_dense {action_dense} |action_sparse raw:={str1}",
- f"|action_dense {action_dense} |action_sparse raw:={str2}",
- f"|action_dense {action_dense} |action_sparse raw:={str3}",
+ f"shared |User_dense {encoded_ctx_str_1} |User_sparse default_ft:={ctx_str_1}",
+ f"|action_dense {action_dense} |action_sparse default_ft:={str1}",
+ f"|action_dense {action_dense} |action_sparse default_ft:={str2}",
+ f"|action_dense {action_dense} |action_sparse default_ft:={str3}",
  ]
  ) # noqa
 
@@ -198,10 +198,10 @@ def test_default_auto_embedder_is_off() -> None:
 
  expected = "\n".join(
  [
- f"shared |User_sparse raw:={ctx_str_1}",
- f"|action_sparse raw:={str1}",
- f"|action_sparse raw:={str2}",
- f"|action_sparse raw:={str3}",
+ f"shared |User_sparse default_ft:={ctx_str_1}",
+ f"|action_sparse default_ft:={str1}",
+ f"|action_sparse default_ft:={str2}",
+ f"|action_sparse default_ft:={str3}",
  ]
  ) # noqa
 
@@ -227,10 +227,10 @@ def test_default_w_embeddings_off() -> None:
 
  expected = "\n".join(
  [
- f"shared |User_sparse raw:={ctx_str_1}",
- f"|action_sparse raw:={str1}",
- f"|action_sparse raw:={str2}",
- f"|action_sparse raw:={str3}",
+ f"shared |User_sparse default_ft:={ctx_str_1}",
+ f"|action_sparse default_ft:={str1}",
+ f"|action_sparse default_ft:={str2}",
+ f"|action_sparse default_ft:={str3}",
  ]
  ) # noqa
 
@@ -258,9 +258,9 @@ def test_default_w_embeddings_on() -> None:
 
  expected = "\n".join(
  [
- f"shared |User_sparse raw:={ctx_str_1} |@_sparse User:={ctx_str_1}",
- f"|action_sparse raw:={str1} |{dot_prod} |#_sparse action:={str1} ",
- f"|action_sparse raw:={str2} |{dot_prod} |#_sparse action:={str2} ",
+ f"shared |User_sparse default_ft:={ctx_str_1} |@_sparse User:={ctx_str_1}",
+ f"|action_sparse default_ft:={str1} |{dot_prod} |#_sparse action:={str1} ",
+ f"|action_sparse default_ft:={str2} |{dot_prod} |#_sparse action:={str2} ",
  ]
  ) # noqa
 

tests/unit_tests/test_pick_best_text_embedder.py

@@ -35,10 +35,10 @@ def test_pickbest_textembedder_no_label_no_emb() -> None:
  named_actions = {"action": ["0", "1", "2"]}
  expected = "\n".join(
  [
- "shared |context_sparse raw:=context",
- "|action_sparse raw:=0",
- "|action_sparse raw:=1",
- "|action_sparse raw:=2",
+ "shared |context_sparse default_ft:=context",
+ "|action_sparse default_ft:=0",
+ "|action_sparse default_ft:=1",
+ "|action_sparse default_ft:=2",
  ]
  )
 
@@ -56,10 +56,10 @@ def test_pickbest_textembedder_w_label_no_score_no_emb() -> None:
  named_actions = {"action": ["0", "1", "2"]}
  expected = "\n".join(
  [
- "shared |context_sparse raw:=context",
- "|action_sparse raw:=0",
- "|action_sparse raw:=1",
- "|action_sparse raw:=2",
+ "shared |context_sparse default_ft:=context",
+ "|action_sparse default_ft:=0",
+ "|action_sparse default_ft:=1",
+ "|action_sparse default_ft:=2",
  ]
  )
  selected = pick_best_chain.PickBestSelected(index=0, probability=1.0)
@@ -80,10 +80,10 @@ def test_pickbest_textembedder_w_full_label_no_emb() -> None:
  named_actions = {"action": ["0", "1", "2"]}
  expected = "\n".join(
  [
- "shared |context_sparse raw:=context",
- "0:-0.0:1.0 |action_sparse raw:=0",
- "|action_sparse raw:=1",
- "|action_sparse raw:=2",
+ "shared |context_sparse default_ft:=context",
+ "0:-0.0:1.0 |action_sparse default_ft:=0",
+ "|action_sparse default_ft:=1",
+ "|action_sparse default_ft:=2",
  ]
  )
 
@@ -142,10 +142,10 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep() -> None:
  context = {"context": rl_chain.EmbedAndKeep(ctx_str)}
  expected = "\n".join(
  [
- f"shared |context_dense {encoded_ctx_str} |context_sparse raw:={ctx_str}",
- "0:-0.0:1.0 |action_dense 0:1.0 1:0.0 |action_sparse raw:=0",
- "|action_dense 0:1.0 1:0.0 |action_sparse raw:=1",
- "|action_dense 0:1.0 1:0.0 |action_sparse raw:=2",
+ f"shared |context_dense {encoded_ctx_str} |context_sparse default_ft:={ctx_str}",
+ "0:-0.0:1.0 |action_dense 0:1.0 1:0.0 |action_sparse default_ft:=0",
+ "|action_dense 0:1.0 1:0.0 |action_sparse default_ft:=1",
+ "|action_dense 0:1.0 1:0.0 |action_sparse default_ft:=2",
  ]
  ) # noqa: E501
  selected = pick_best_chain.PickBestSelected(index=0, probability=1.0, score=0.0)
@@ -164,10 +164,10 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb() -> None:
  context = {"context1": "context1", "context2": "context2"}
  expected = "\n".join(
  [
- "shared |context1_sparse raw:=context1 |context2_sparse raw:=context2 ",
- "|a_sparse raw:=0 |b_sparse raw:=0",
- "|action1_sparse raw:=1",
- "|action1_sparse raw:=2",
+ "shared |context1_sparse default_ft:=context1 |context2_sparse default_ft:=context2 ",
+ "|a_sparse default_ft:=0 |b_sparse default_ft:=0",
+ "|action1_sparse default_ft:=1",
+ "|action1_sparse default_ft:=2",
  ]
  ) # noqa: E501
  event = pick_best_chain.PickBestEvent(
@@ -185,10 +185,10 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb() -> None:
  context = {"context1": "context1", "context2": "context2"}
  expected = "\n".join(
  [
- "shared |context1_sparse raw:=context1 |context2_sparse raw:=context2",
- "|a_sparse raw:=0 |b_sparse raw:=0",
- "|action_sparse raw:=1",
- "|action_sparse raw:=2",
+ "shared |context1_sparse default_ft:=context1 |context2_sparse default_ft:=context2",
+ "|a_sparse default_ft:=0 |b_sparse default_ft:=0",
+ "|action_sparse default_ft:=1",
+ "|action_sparse default_ft:=2",
  ]
  ) # noqa: E501
  selected = pick_best_chain.PickBestSelected(index=0, probability=1.0)
@@ -207,10 +207,10 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb() -> None:
  context = {"context1": "context1", "context2": "context2"}
  expected = "\n".join(
  [
- "shared |context1_sparse raw:=context1 |context2_sparse raw:=context2",
- "0:-0.0:1.0 |a_sparse raw:=0 |b_sparse raw:=0",
- "|action_sparse raw:=1",
- "|action_sparse raw:=2",
+ "shared |context1_sparse default_ft:=context1 |context2_sparse default_ft:=context2",
+ "0:-0.0:1.0 |a_sparse default_ft:=0 |b_sparse default_ft:=0",
+ "|action_sparse default_ft:=1",
+ "|action_sparse default_ft:=2",
  ]
  ) # noqa: E501
  selected = pick_best_chain.PickBestSelected(index=0, probability=1.0, score=0.0)
@@ -282,10 +282,10 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
  }
  expected = "\n".join(
  [
- f"shared |context1_dense {encoded_ctx_str_1} |context2_dense {encoded_ctx_str_2} |context1_sparse raw:={ctx_str_1} |context2_sparse raw:={ctx_str_2}",
- f"0:-0.0:1.0 |a_dense 0:1.0 1:0.0 |b_dense 0:1.0 1:0.0 |a_sparse raw:=0 |b_sparse raw:=0",
- f"|action_dense 0:1.0 1:0.0 |action_sparse raw:=1",
- f"|action_dense 0:1.0 1:0.0 |action_sparse raw:=2",
+ f"shared |context1_dense {encoded_ctx_str_1} |context2_dense {encoded_ctx_str_2} |context1_sparse default_ft:={ctx_str_1} |context2_sparse default_ft:={ctx_str_2}",
+ f"0:-0.0:1.0 |a_dense 0:1.0 1:0.0 |b_dense 0:1.0 1:0.0 |a_sparse default_ft:=0 |b_sparse default_ft:=0",
+ f"|action_dense 0:1.0 1:0.0 |action_sparse default_ft:=1",
+ f"|action_dense 0:1.0 1:0.0 |action_sparse default_ft:=2",
  ]
  ) # noqa: E501
 
@@ -317,9 +317,9 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb() -> N
 
  expected = "\n".join(
  [
- f"shared |context2_dense {encoded_ctx_str_2} |context1_sparse raw:={ctx_str_1}",
- f"0:-0.0:1.0 |b_dense 0:1.0 1:0.0 |a_sparse raw:=0",
- f"|action_sparse raw:=1",
+ f"shared |context2_dense {encoded_ctx_str_2} |context1_sparse default_ft:={ctx_str_1}",
+ f"0:-0.0:1.0 |b_dense 0:1.0 1:0.0 |a_sparse default_ft:=0",
+ f"|action_sparse default_ft:=1",
  f"|action_dense 0:1.0 1:0.0",
  ]
  ) # noqa: E501
@@ -355,10 +355,10 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emakeep()
  context = {"context1": ctx_str_1, "context2": rl_chain.EmbedAndKeep(ctx_str_2)}
  expected = "\n".join(
  [
- f"shared |context2_dense {encoded_ctx_str_2} |context1_sparse raw:={ctx_str_1} |context2_sparse raw:={ctx_str_2}",
- f"0:-0.0:1.0 |b_dense 0:1.0 1:0.0 |a_sparse raw:=0 |b_sparse raw:=0",
- f"|action_sparse raw:=1",
- f"|action_dense 0:1.0 1:0.0 |action_sparse raw:=2",
+ f"shared |context2_dense {encoded_ctx_str_2} |context1_sparse default_ft:={ctx_str_1} |context2_sparse default_ft:={ctx_str_2}",
+ f"0:-0.0:1.0 |b_dense 0:1.0 1:0.0 |a_sparse default_ft:=0 |b_sparse default_ft:=0",
+ f"|action_sparse default_ft:=1",
+ f"|action_dense 0:1.0 1:0.0 |action_sparse default_ft:=2",
  ]
  ) # noqa: E501
  selected = pick_best_chain.PickBestSelected(index=0, probability=1.0, score=0.0)
@@ -386,8 +386,8 @@ def test_raw_features_underscored() -> None:
  context = {"context": ctx_str}
  expected_no_embed = "\n".join(
  [
- f"shared |context_sparse raw:={ctx_str_underscored}",
- f"|action_sparse raw:={str1_underscored}",
+ f"shared |context_sparse default_ft:={ctx_str_underscored}",
+ f"|action_sparse default_ft:={str1_underscored}",
  ]
  )
 
@@ -414,8 +414,8 @@ def test_raw_features_underscored() -> None:
  context = {"context": rl_chain.EmbedAndKeep(ctx_str)}
  expected_embed_and_keep = "\n".join(
  [
- f"shared |context_dense {encoded_ctx_str} |context_sparse raw:={ctx_str_underscored}",
- f"|action_dense {encoded_str1} |action_sparse raw:={str1_underscored}",
+ f"shared |context_dense {encoded_ctx_str} |context_sparse default_ft:={ctx_str_underscored}",
+ f"|action_dense {encoded_str1} |action_sparse default_ft:={str1_underscored}",
  ]
  ) # noqa: E501
  event = pick_best_chain.PickBestEvent(