metric / task.py
Elron's picture
Upload task.py with huggingface_hub
a8f310f
raw
history blame
No virus
3.31 kB
from typing import Any, Dict, List, Optional
from .operator import StreamInstanceOperator
class Tasker:
pass
class FormTask(Tasker, StreamInstanceOperator):
inputs: List[str]
outputs: List[str]
metrics: List[str]
augmentable_inputs: List[str] = []
def verify(self):
for augmentable_input in self.augmentable_inputs:
assert (
augmentable_input in self.inputs
), f"augmentable_input f{augmentable_input} is not part of {self.inputs}"
def process(
self, instance: Dict[str, Any], stream_name: Optional[str] = None
) -> Dict[str, Any]:
try:
inputs = {key: instance[key] for key in self.inputs}
except KeyError as e:
raise KeyError(
f"Unexpected FormTask input column names ({[key for key in self.inputs if key not in instance]})."
f"The available input names: {list(instance.keys())}"
) from e
try:
outputs = {key: instance[key] for key in self.outputs}
except KeyError as e:
raise KeyError(
f"Unexpected FormTask output column names: {[key for key in self.outputs if key not in instance]}"
f" \n available names:{list(instance.keys())}\n given output names:{self.outputs}"
) from e
return {
"inputs": inputs,
"outputs": outputs,
"metrics": self.metrics,
}
class MultipleChoiceTask(FormTask):
choices_field: str = "choices"
choices_separator: str = "\n"
enumeration_suffix: str = ". "
use_text_in_target: bool = False
alphabet: str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def process_single_choice(
self, choice: str, index: int, use_text: bool = True
) -> str:
try:
processed_choice = f"{self.alphabet[index]}"
except IndexError as e:
raise ValueError(
f"Too many choices, the length of alphabet '{self.alphabet}': {len(self.alphabet)} is the limit"
) from e
if use_text:
processed_choice += f"{self.enumeration_suffix}{choice}"
return processed_choice
def process_choices(self, choices: List[str]) -> str:
processed_choices = []
for index, choice in enumerate(choices):
processed_choices.append(self.process_single_choice(choice, index))
return self.choices_separator.join(processed_choices)
def process_target(self, choices, target_index):
return self.process_single_choice(
choices[target_index], target_index, use_text=self.use_text_in_target
)
def process(
self, instance: Dict[str, Any], stream_name: Optional[str] = None
) -> Dict[str, Any]:
result = super().process(instance, stream_name)
target_key, target_value = next(iter(result["outputs"].items()))
choices = result["inputs"][self.choices_field]
target_index_in_choices = choices.index(target_value)
processed_choices = self.process_choices(choices)
processed_target = self.process_target(choices, target_index_in_choices)
result["inputs"][self.choices_field] = processed_choices
result["outputs"][target_key] = processed_target
return result