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Make task management optional #535

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Aug 12, 2023
Merged

Make task management optional #535

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105 changes: 62 additions & 43 deletions src/autolabel/labeler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,8 +53,10 @@ def __init__(
config: Union[AutolabelConfig, str, dict],
cache: Optional[bool] = True,
example_selector: Optional[BaseExampleSelector] = None,
create_task: Optional[bool] = True,
) -> None:
self.db = StateManager()
self.create_task = create_task
self.db = StateManager() if self.create_task else None
self.generation_cache = SQLAlchemyGenerationCache() if cache else None
self.transform_cache = SQLAlchemyTransformCache() if cache else None

Expand All @@ -70,6 +72,9 @@ def __init__(
)
self.example_selector = example_selector

# Only used if we don't use task management
self.all_annotations = []

if in_notebook():
import nest_asyncio

Expand All @@ -95,9 +100,13 @@ def run(

dataset = dataset.get_slice(max_items=max_items, start_index=start_index)

self.db.initialize()
self.dataset_obj = self.db.initialize_dataset(dataset.df, self.config)
self.task_object = self.db.initialize_task(self.config)
if self.create_task:
self.db.initialize()
self.dataset_obj = self.db.initialize_dataset(dataset.df, self.config)
self.task_object = self.db.initialize_task(self.config)
else:
self.all_annotations = []

if isinstance(dataset, str):
csv_file_name = (
output_name
Expand All @@ -107,21 +116,24 @@ def run(
else:
csv_file_name = f"{self.config.task_name()}_labeled.csv"

# Initialize task run and check if it already exists
self.task_run = self.db.get_task_run(self.task_object.id, self.dataset_obj.id)
# Resume/Delete the task if it already exists or create a new task run
if self.task_run:
logger.info("Task run already exists.")
self.task_run = self.handle_existing_task_run(
self.task_run,
csv_file_name,
gt_labels=dataset.gt_labels,
additional_metrics=additional_metrics,
)
else:
self.task_run = self.db.create_task_run(
csv_file_name, self.task_object.id, self.dataset_obj.id
if self.create_task:
# Initialize task run and check if it already exists
self.task_run = self.db.get_task_run(
self.task_object.id, self.dataset_obj.id
)
# Resume/Delete the task if it already exists or create a new task run
if self.task_run:
logger.info("Task run already exists.")
self.task_run = self.handle_existing_task_run(
self.task_run,
csv_file_name,
gt_labels=dataset.gt_labels,
additional_metrics=additional_metrics,
)
else:
self.task_run = self.db.create_task_run(
csv_file_name, self.task_object.id, self.dataset_obj.id
)

# Get the seed examples from the dataset config
seed_examples = self.config.few_shot_example_set()
Expand Down Expand Up @@ -149,8 +161,7 @@ def run(
cache=self.generation_cache is not None,
)

num_failures = 0
current_index = self.task_run.current_index
current_index = self.task_run.current_index if self.create_task else 0
cost = 0.0
postfix_dict = {}

Expand Down Expand Up @@ -201,23 +212,15 @@ def run(
annotations.append(annotation)
annotation = self.majority_annotation(annotations)

# Store the annotation in the database
AnnotationModel.create_from_llm_annotation(
self.db.session,
annotation,
current_index + i,
self.task_run.id,
)
# Save the annotation in the database
self.save_annotation(annotation, current_index, i)

cost += sum(response.costs)
postfix_dict[self.COST_KEY] = f"{cost:.2f}"

# Evaluate the task every eval_every examples
if (current_index + 1) % eval_every == 0:
db_result = AnnotationModel.get_annotations_by_task_run_id(
self.db.session, self.task_run.id
)
llm_labels = [pickle.loads(a.llm_annotation) for a in db_result]
llm_labels = self.get_all_annotations()
if dataset.gt_labels:
eval_result = self.task.eval(
llm_labels,
Expand All @@ -236,15 +239,13 @@ def run(
else m.value
)

# Update task run state
self.task_run = self.save_task_run_state(
current_index=current_index + len(chunk)
)
if self.create_task:
# Update task run state
self.task_run = self.save_task_run_state(
current_index=current_index + len(chunk)
)

db_result = AnnotationModel.get_annotations_by_task_run_id(
self.db.session, self.task_run.id
)
llm_labels = [pickle.loads(a.llm_annotation) for a in db_result]
llm_labels = self.get_all_annotations()
eval_result = None
# if true labels are provided, evaluate accuracy of predictions
if dataset.gt_labels:
Expand Down Expand Up @@ -396,10 +397,7 @@ def handle_existing_task_run(
gt_labels: If ground truth labels are provided, performance metrics will be displayed, such as label accuracy
"""
pprint(f"There is an existing task with following details: {task_run}")
db_result = AnnotationModel.get_annotations_by_task_run_id(
self.db.session, task_run.id
)
llm_labels = [pickle.loads(a.llm_annotation) for a in db_result]
llm_labels = self.get_all_annotations()
if gt_labels and len(llm_labels) > 0:
pprint("Evaluating the existing task...")
gt_labels = gt_labels[: len(llm_labels)]
Expand Down Expand Up @@ -501,3 +499,24 @@ def clear_cache(self, use_ttl: bool = True):
"""
self.generation_cache.clear(use_ttl=use_ttl)
self.transform_cache.clear(use_ttl=use_ttl)

def save_annotation(self, annotation: LLMAnnotation, current_index: int, i: int):
if self.create_task:
# Store the annotation in the database
AnnotationModel.create_from_llm_annotation(
self.db.session,
annotation,
current_index + i,
self.task_run.id,
)
else:
self.all_annotations.append(annotation)

def get_all_annotations(self):
if self.create_task:
db_result = AnnotationModel.get_annotations_by_task_run_id(
self.db.session, self.task_run.id
)
return [pickle.loads(a.llm_annotation) for a in db_result]
else:
return self.all_annotations
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