sij/khoj
1
0
Fork 0
mirror of https://github.com/khoj-ai/khoj.git synced 2024-11-23 15:38:55 +01:00
Code Issues Projects Releases Packages Wiki Activity Actions 5

Show correct example index being currently processed in frames eval

Browse source 
Previously the batch start index wasn't being passed so all batches
started in parallel were showing the same processing example index

This change doesn't impact the evaluation itself, just the index shown
of the example currently being evaluated
This commit is contained in:
Debanjum 2024-11-08 15:46:44 -08:00
parent d7fc4a91d5
commit a62ff614fb
1 changed files with 7 additions and 6 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
tests/eval_frames.py
View file

@ -101,10 +101,10 @@ def evaluate_response(query: str, agent_response: str, ground_truth: str) -> Dic
return {"decision": "FALSE", "explanation": f"Evaluation failed: {str(e)}"}
def process_batch(batch, counter, results, dataset_length):
for prompt, answer, reasoning_type in batch:
counter += 1
logger.info(f"Processing example: {counter}/{dataset_length}")
def process_batch(batch, batch_start, results, dataset_length):
for idx, (prompt, answer, reasoning_type) in enumerate(batch):
current_index = batch_start + idx
logger.info(f"Processing example: {current_index}/{dataset_length}")
# Trigger research mode if enabled
prompt = f"/{KHOJ_MODE} {prompt}" if KHOJ_MODE else prompt
@ -122,7 +122,7 @@ def process_batch(batch, counter, results, dataset_length):
# Store results
results.append(
{
"index": counter,
"index": current_index,
"prompt": prompt,
"ground_truth": answer,
"agent_response": agent_response,
@ -169,12 +169,13 @@ def main():
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = []
for i in range(0, dataset_length, BATCH_SIZE):
batch_start = i
batch = zip(
dataset["Prompt"][i : i + BATCH_SIZE],
dataset["Answer"][i : i + BATCH_SIZE],
dataset["reasoning_types"][i : i + BATCH_SIZE],
)
futures.append(executor.submit(process_batch, batch, counter, results, dataset_length))
futures.append(executor.submit(process_batch, batch, batch_start, results, dataset_length))
# Wait for all futures to complete
concurrent.futures.wait(futures)