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dot product of encodings as default auto_embed

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This commit is contained in:
olgavrou 2023-09-04 05:50:15 -04:00
parent a9ba6a8cd1
commit b162f1c8e1
4 changed files with 163 additions and 47 deletions
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7
libs/langchain/langchain/chains/rl_chain/base.py
View File

@ -229,6 +229,9 @@ class VwPolicy(Policy):
class Embedder(Generic[TEvent], ABC):
def __init__(self, *args: Any, **kwargs: Any):
pass
@abstractmethod
def format(self, event: TEvent) -> str:
...
@ -498,8 +501,8 @@ class RLChain(Chain, Generic[TEvent]):
) -> Dict[str, Any]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
if self.auto_embed:
inputs = prepare_inputs_for_autoembed(inputs=inputs)
# if self.auto_embed:
# inputs = prepare_inputs_for_autoembed(inputs=inputs)
event: TEvent = self._call_before_predict(inputs=inputs)
prediction = self.active_policy.predict(event=event)

133
libs/langchain/langchain/chains/rl_chain/pick_best_chain.py
View File

@ -53,21 +53,25 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
model name (Any, optional): The type of embeddings to be used for feature representation. Defaults to BERT SentenceTransformer.
""" # noqa E501
def __init__(self, model: Optional[Any] = None, *args: Any, **kwargs: Any):
def __init__(
self, auto_embed: bool, model: Optional[Any] = None, *args: Any, **kwargs: Any
):
super().__init__(*args, **kwargs)
if model is None:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("bert-base-nli-mean-tokens")
model = SentenceTransformer("all-mpnet-base-v2")
# model = SentenceTransformer("all-MiniLM-L6-v2")
self.model = model
self.auto_embed = auto_embed
def format(self, event: PickBestEvent) -> str:
"""
Converts the `BasedOn` and `ToSelectFrom` into a format that can be used by VW
"""
@staticmethod
def _str(embedding):
return " ".join([f"{i}:{e}" for i, e in enumerate(embedding)])
def get_label(self, event: PickBestEvent) -> tuple:
cost = None
if event.selected:
chosen_action = event.selected.index
@ -77,7 +81,11 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
else None
)
prob = event.selected.probability
return chosen_action, cost, prob
else:
return None, None, None
def get_context_and_action_embeddings(self, event: PickBestEvent) -> tuple:
context_emb = base.embed(event.based_on, self.model) if event.based_on else None
to_select_from_var_name, to_select_from = next(
iter(event.to_select_from.items()), (None, None)
@ -97,6 +105,97 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
raise ValueError(
"Context and to_select_from must be provided in the inputs dictionary"
)
return context_emb, action_embs
def get_indexed_dot_product(self, context_emb: List, action_embs: List) -> Dict:
import numpy as np
unique_contexts = set()
for context_item in context_emb:
for ns, ee in context_item.items():
if isinstance(ee, list):
for ea in ee:
unique_contexts.add(f"{ns}={ea}")
else:
unique_contexts.add(f"{ns}={ee}")
encoded_contexts = self.model.encode(list(unique_contexts))
context_embeddings = dict(zip(unique_contexts, encoded_contexts))
unique_actions = set()
for action in action_embs:
for ns, e in action.items():
if isinstance(e, list):
for ea in e:
unique_actions.add(f"{ns}={ea}")
else:
unique_actions.add(f"{ns}={e}")
encoded_actions = self.model.encode(list(unique_actions))
action_embeddings = dict(zip(unique_actions, encoded_actions))
action_matrix = np.stack([v for k, v in action_embeddings.items()])
context_matrix = np.stack([v for k, v in context_embeddings.items()])
dot_product_matrix = np.dot(context_matrix, action_matrix.T)
indexed_dot_product = {}
for i, context_key in enumerate(context_embeddings.keys()):
indexed_dot_product[context_key] = {}
for j, action_key in enumerate(action_embeddings.keys()):
indexed_dot_product[context_key][action_key] = dot_product_matrix[i, j]
return indexed_dot_product
def format_auto_embed_on(self, event: PickBestEvent) -> str:
chosen_action, cost, prob = self.get_label(event)
context_emb, action_embs = self.get_context_and_action_embeddings(event)
indexed_dot_product = self.get_indexed_dot_product(context_emb, action_embs)
action_lines = []
for i, action in enumerate(action_embs):
line_parts = []
dot_prods = []
if cost is not None and chosen_action == i:
line_parts.append(f"{chosen_action}:{cost}:{prob}")
for ns, action in action.items():
line_parts.append(f"|{ns}")
elements = action if isinstance(action, list) else [action]
nsa = []
for elem in elements:
line_parts.append(f"{elem}")
ns_a = f"{ns}={elem}"
nsa.append(ns_a)
for k,v in indexed_dot_product.items():
dot_prods.append(v[ns_a])
nsa = " ".join(nsa)
line_parts.append(f"|# {nsa}")
line_parts.append(f"|embedding {self._str(dot_prods)}")
action_lines.append(" ".join(line_parts))
shared = []
for item in context_emb:
for ns, context in item.items():
shared.append(f"|{ns}")
elements = context if isinstance(context, list) else [context]
nsc = []
for elem in elements:
shared.append(f"{elem}")
nsc.append(f"{ns}={elem}")
nsc = " ".join(nsc)
shared.append(f"|@ {nsc}")
r = "shared " + " ".join(shared) + "\n" + "\n".join(action_lines)
print(r)
return r
def format_auto_embed_off(self, event: PickBestEvent) -> str:
"""
Converts the `BasedOn` and `ToSelectFrom` into a format that can be used by VW
"""
chosen_action, cost, prob = self.get_label(event)
context_emb, action_embs = self.get_context_and_action_embeddings(event)
example_string = ""
example_string += "shared "
@ -120,6 +219,12 @@ class PickBestFeatureEmbedder(base.Embedder[PickBestEvent]):
# Strip the last newline
return example_string[:-1]
def format(self, event: PickBestEvent) -> str:
if self.auto_embed:
return self.format_auto_embed_on(event)
else:
return self.format_auto_embed_off(event)
class PickBest(base.RLChain[PickBestEvent]):
"""
@ -154,12 +259,20 @@ class PickBest(base.RLChain[PickBestEvent]):
*args: Any,
**kwargs: Any,
):
auto_embed = kwargs.get("auto_embed", False)
vw_cmd = kwargs.get("vw_cmd", [])
if not vw_cmd:
vw_cmd = [
interactions = ["--interactions=::"]
if auto_embed:
interactions = [
"--interactions=@#",
"--ignore_linear=@",
"--ignore_linear=#",
"--noconstant",
]
vw_cmd = interactions + [
"--cb_explore_adf",
"--quiet",
"--interactions=::",
"--coin",
"--squarecb",
]
@ -172,7 +285,7 @@ class PickBest(base.RLChain[PickBestEvent]):
feature_embedder = kwargs.get("feature_embedder", None)
if not feature_embedder:
feature_embedder = PickBestFeatureEmbedder()
feature_embedder = PickBestFeatureEmbedder(auto_embed=auto_embed)
kwargs["feature_embedder"] = feature_embedder
super().__init__(*args, **kwargs)

40
libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_chain_call.py
View File

@ -26,7 +26,7 @@ def test_multiple_ToSelectFrom_throws() -> None:
chain = pick_best_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
with pytest.raises(ValueError):
@ -43,7 +43,7 @@ def test_missing_basedOn_from_throws() -> None:
chain = pick_best_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
with pytest.raises(ValueError):
@ -56,7 +56,7 @@ def test_ToSelectFrom_not_a_list_throws() -> None:
chain = pick_best_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = {"actions": ["0", "1", "2"]}
with pytest.raises(ValueError):
@ -75,7 +75,7 @@ def test_update_with_delayed_score_with_auto_validator_throws() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
response = chain.run(
@ -98,7 +98,7 @@ def test_update_with_delayed_score_force() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=rl_chain.AutoSelectionScorer(llm=auto_val_llm),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
response = chain.run(
@ -121,7 +121,7 @@ def test_update_with_delayed_score() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=None,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
response = chain.run(
@ -153,7 +153,7 @@ def test_user_defined_scorer() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=CustomSelectionScorer(),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
actions = ["0", "1", "2"]
response = chain.run(
@ -166,11 +166,11 @@ def test_user_defined_scorer() -> None:
@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
def test_auto_embeddings_on() -> None:
def test_everything_embedded() -> None:
llm, PROMPT = setup()
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
chain = pick_best_chain.PickBest.from_llm(
llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
)
str1 = "0"
@ -189,8 +189,8 @@ def test_auto_embeddings_on() -> None:
actions = [str1, str2, str3]
response = chain.run(
User=rl_chain.BasedOn(ctx_str_1),
action=rl_chain.ToSelectFrom(actions),
User=rl_chain.EmbedAndKeep(rl_chain.BasedOn(ctx_str_1)),
action=rl_chain.EmbedAndKeep(rl_chain.ToSelectFrom(actions)),
)
selection_metadata = response["selection_metadata"]
vw_str = feature_embedder.format(selection_metadata)
@ -200,7 +200,7 @@ def test_auto_embeddings_on() -> None:
@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
def test_default_auto_embedder_is_off() -> None:
llm, PROMPT = setup()
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
chain = pick_best_chain.PickBest.from_llm(
llm=llm, prompt=PROMPT, feature_embedder=feature_embedder
)
@ -226,7 +226,7 @@ def test_default_auto_embedder_is_off() -> None:
@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
def test_default_embeddings_off() -> None:
llm, PROMPT = setup()
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
chain = pick_best_chain.PickBest.from_llm(
llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=False
)
@ -252,7 +252,7 @@ def test_default_embeddings_off() -> None:
@pytest.mark.requires("vowpal_wabbit_next", "sentence_transformers")
def test_default_embeddings_mixed_w_explicit_user_embeddings() -> None:
llm, PROMPT = setup()
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=True, model=MockEncoder())
chain = pick_best_chain.PickBest.from_llm(
llm=llm, prompt=PROMPT, feature_embedder=feature_embedder, auto_embed=True
)
@ -291,7 +291,7 @@ def test_default_no_scorer_specified() -> None:
chain = pick_best_chain.PickBest.from_llm(
llm=chain_llm,
prompt=PROMPT,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
response = chain.run(
User=rl_chain.BasedOn("Context"),
@ -310,7 +310,7 @@ def test_explicitly_no_scorer() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=None,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
response = chain.run(
User=rl_chain.BasedOn("Context"),
@ -330,7 +330,7 @@ def test_auto_scorer_with_user_defined_llm() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=rl_chain.AutoSelectionScorer(llm=scorer_llm),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
response = chain.run(
User=rl_chain.BasedOn("Context"),
@ -348,7 +348,7 @@ def test_calling_chain_w_reserved_inputs_throws() -> None:
chain = pick_best_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
with pytest.raises(ValueError):
chain.run(
@ -371,7 +371,7 @@ def test_activate_and_deactivate_scorer() -> None:
llm=llm,
prompt=PROMPT,
selection_scorer=pick_best_chain.base.AutoSelectionScorer(llm=scorer_llm),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder()),
feature_embedder=pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder()),
)
response = chain.run(
User=pick_best_chain.base.BasedOn("Context"),

30
libs/langchain/tests/unit_tests/chains/rl_chain/test_pick_best_text_embedder.py
View File

@ -9,7 +9,7 @@ encoded_keyword = "[encoded]"
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_missing_context_throws() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_action = {"action": ["0", "1", "2"]}
event = pick_best_chain.PickBestEvent(
inputs={}, to_select_from=named_action, based_on={}
@ -20,7 +20,7 @@ def test_pickbest_textembedder_missing_context_throws() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_missing_actions_throws() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
event = pick_best_chain.PickBestEvent(
inputs={}, to_select_from={}, based_on={"context": "context"}
)
@ -30,7 +30,7 @@ def test_pickbest_textembedder_missing_actions_throws() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_no_label_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": ["0", "1", "2"]}
expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
event = pick_best_chain.PickBestEvent(
@ -42,7 +42,7 @@ def test_pickbest_textembedder_no_label_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_w_label_no_score_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": ["0", "1", "2"]}
expected = """shared |context context \n|action1 0 \n|action1 1 \n|action1 2 """
selected = pick_best_chain.PickBestSelected(index=0, probability=1.0)
@ -58,7 +58,7 @@ def test_pickbest_textembedder_w_label_no_score_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_w_full_label_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": ["0", "1", "2"]}
expected = (
"""shared |context context \n0:-0.0:1.0 |action1 0 \n|action1 1 \n|action1 2 """
@ -76,7 +76,7 @@ def test_pickbest_textembedder_w_full_label_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_w_full_label_w_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
str3 = "2"
@ -100,7 +100,7 @@ def test_pickbest_textembedder_w_full_label_w_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_w_full_label_w_embed_and_keep() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
str3 = "2"
@ -124,7 +124,7 @@ def test_pickbest_textembedder_w_full_label_w_embed_and_keep() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_no_label_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
context = {"context1": "context1", "context2": "context2"}
expected = """shared |context1 context1 |context2 context2 \n|a 0 |b 0 \n|action1 1 \n|action1 2 """ # noqa: E501
@ -137,7 +137,7 @@ def test_pickbest_textembedder_more_namespaces_no_label_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_w_label_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
context = {"context1": "context1", "context2": "context2"}
expected = """shared |context1 context1 |context2 context2 \n|a 0 |b 0 \n|action1 1 \n|action1 2 """ # noqa: E501
@ -151,7 +151,7 @@ def test_pickbest_textembedder_more_namespaces_w_label_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
named_actions = {"action1": [{"a": "0", "b": "0"}, "1", "2"]}
context = {"context1": "context1", "context2": "context2"}
expected = """shared |context1 context1 |context2 context2 \n0:-0.0:1.0 |a 0 |b 0 \n|action1 1 \n|action1 2 """ # noqa: E501
@ -165,7 +165,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_no_emb() -> None:
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
@ -198,7 +198,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_emb() -> None
def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_keep() -> (
None
):
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
@ -231,7 +231,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_full_embed_and_kee
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
@ -263,7 +263,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emb() -> N
@pytest.mark.requires("vowpal_wabbit_next")
def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emakeep() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "0"
str2 = "1"
@ -298,7 +298,7 @@ def test_pickbest_textembedder_more_namespaces_w_full_label_w_partial_emakeep()
@pytest.mark.requires("vowpal_wabbit_next")
def test_raw_features_underscored() -> None:
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(model=MockEncoder())
feature_embedder = pick_best_chain.PickBestFeatureEmbedder(auto_embed=False, model=MockEncoder())
str1 = "this is a long string"
str1_underscored = str1.replace(" ", "_")
encoded_str1 = rl_chain.stringify_embedding(list(encoded_keyword + str1))