def _calculate_attributions(self, embeddings: Embedding): # type: ignore
(
self.input_ids,
self.ref_input_ids,
self.sep_idx,
) = self._make_input_reference_pair(self.question, self.text)
(
self.position_ids,
self.ref_position_ids,
) = self._make_input_reference_position_id_pair(self.input_ids)
(
self.token_type_ids,
self.ref_token_type_ids,
) = self._make_input_reference_token_type_pair(self.input_ids, self.sep_idx)
self.attention_mask = self._make_attention_mask(self.input_ids)
if self.attribution_type == "lig":
reference_tokens = [
token.replace("Ġ", "") for token in self.decode(self.input_ids)
]
self.position = 0
start_lig = LIGAttributions(
self._forward,
embeddings,
reference_tokens,
self.input_ids,
self.ref_input_ids,
self.sep_idx,
self.attention_mask,
position_ids=self.position_ids,
ref_position_ids=self.ref_position_ids,
token_type_ids=self.token_type_ids,
ref_token_type_ids=self.ref_token_type_ids,
)
start_lig.summarize()
self.start_attributions = start_lig
self.position = 1
end_lig = LIGAttributions(
self._forward,
embeddings,
reference_tokens,
self.input_ids,
self.ref_input_ids,
self.sep_idx,
self.attention_mask,
position_ids=self.position_ids,
ref_position_ids=self.ref_position_ids,
token_type_ids=self.token_type_ids,
ref_token_type_ids=self.ref_token_type_ids,
)
end_lig.summarize()
self.end_attributions = end_lig
self.attributions = [self.start_attributions, self.end_attributions]
else:
pass
def _calculate_attributions( # type: ignore
self,
embeddings: Embedding,
index: int = None,
class_name: str = None):
(
self.input_ids,
self.ref_input_ids,
self.sep_idx,
) = self._make_input_reference_pair(self.text)
(
self.position_ids,
self.ref_position_ids,
) = self._make_input_reference_position_id_pair(self.input_ids)
self.attention_mask = self._make_attention_mask(self.input_ids)
if index is not None:
self.selected_index = index
elif class_name is not None:
if class_name in self.label2id.keys():
self.selected_index = self.label2id[class_name]
else:
s = f"'{class_name}' is not found in self.label2id keys."
s += "Defaulting to predicted index instead."
warnings.warn(s)
self.selected_index = self.predicted_class_index
else:
self.selected_index = self.predicted_class_index
if self.attribution_type == "lig":
reference_tokens = [
token.replace("Ġ", "") for token in self.decode(self.input_ids)
]
lig = LIGAttributions(
self._forward,
embeddings,
reference_tokens,
self.input_ids,
self.ref_input_ids,
self.sep_idx,
self.attention_mask,
position_ids=self.position_ids,
ref_position_ids=self.ref_position_ids,
)
lig.summarize()
self.attributions = lig
else:
pass
def _calculate_attributions(self,
index: int = None,
class_name: str = None):
(
self.input_ids,
self.ref_input_ids,
self.sep_idx,
) = self._make_input_reference_pair(self.text)
if index is not None:
self.selected_index = index
elif class_name is not None:
if class_name in self.label2id.keys():
self.selected_index = self.label2id[class_name]
else:
s = f"'{class_name}' is not found in self.label2id keys."
s += "Defaulting to predicted index instead."
warnings.warn(s)
self.selected_index = self.predicted_class_index
else:
self.selected_index = self.predicted_class_index
if self.attribution_type == "lig":
embeddings = getattr(self.model, self.model_type).embeddings
reference_text = "BOS_TOKEN " + self.text + " EOS_TOKEN"
lig = LIGAttributions(
self._forward,
embeddings,
reference_text,
self.input_ids,
self.ref_input_ids,
self.sep_idx,
)
lig.summarize()
self.attributions = lig
else:
pass
def _calculate_attributions(self,
embeddings: Embedding,
class_name: str,
index: int = None): # type: ignore
(
self.input_ids,
self.ref_input_ids,
self.sep_idx,
) = self._make_input_reference_pair(self.text, self.hypothesis_text)
(
self.position_ids,
self.ref_position_ids,
) = self._make_input_reference_position_id_pair(self.input_ids)
(
self.token_type_ids,
self.ref_token_type_ids,
) = self._make_input_reference_token_type_pair(self.input_ids,
self.sep_idx)
self.attention_mask = self._make_attention_mask(self.input_ids)
self.selected_index = int(self.label2id[class_name])
reference_tokens = [
token.replace("Ġ", "") for token in self.decode(self.input_ids)
]
lig = LIGAttributions(
self._forward,
embeddings,
reference_tokens,
self.input_ids,
self.ref_input_ids,
self.sep_idx,
self.attention_mask,
position_ids=self.position_ids,
ref_position_ids=self.ref_position_ids,
token_type_ids=self.token_type_ids,
ref_token_type_ids=self.ref_token_type_ids,
internal_batch_size=self.internal_batch_size,
n_steps=self.n_steps,
)
if self.include_hypothesis:
lig.summarize()
else:
lig.summarize(self.sep_idx)
self.attributions.append(lig)