apunkt/MetaGPT
1
0
Fork 0
mirror of https://github.com/FoundationAgents/MetaGPT.git synced 2026-05-02 20:32:38 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

code adapted to v0.6

Browse source
This commit is contained in:
yzlin 2024-01-10 17:20:01 +08:00
parent 4ec6151691
commit cd990fd5c9
15 changed files with 80 additions and 77 deletions
Download patch file Download diff file Expand all files Collapse all files

6
tests/metagpt/actions/test_write_plan.py
View file

@ -1,8 +1,4 @@
from metagpt.actions.write_plan import (
Plan,
Task,
precheck_update_plan_from_rsp,
)
from metagpt.actions.write_plan import Plan, Task, precheck_update_plan_from_rsp
def test_precheck_update_plan_from_rsp():

7
tests/metagpt/roles/run_code_interpreter.py
View file

@ -1,6 +1,7 @@
import fire
from metagpt.actions.execute_code import ExecutePyCode
from metagpt.const import DATA_PATH
from metagpt.logs import logger
from metagpt.roles.code_interpreter import CodeInterpreter
from metagpt.roles.ml_engineer import MLEngineer
@ -53,10 +54,10 @@ async def run_code_interpreter(
if __name__ == "__main__":
requirement = "Run data analysis on sklearn Iris dataset, include a plot"
# requirement = "Run data analysis on sklearn Iris dataset, include a plot"
# requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy"
# data_path = f"{DATA_PATH}/titanic"
# requirement = f"This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: '{data_path}/split_train.csv', eval data path: '{data_path}/split_eval.csv'."
data_path = f"{DATA_PATH}/titanic"
requirement = f"This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: '{data_path}/split_train.csv', eval data path: '{data_path}/split_eval.csv'."
# data_path = f"{DATA_PATH}/icr-identify-age-related-conditions"
# requirement = f"This is a medical dataset with over fifty anonymized health characteristics linked to three age-related conditions. Your goal is to predict whether a subject has or has not been diagnosed with one of these conditions.The target column is Class. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report f1 score on the eval data. Train data path: {data_path}/split_train.csv, eval data path: {data_path}/split_eval.csv."
# data_path = f"{DATA_PATH}/santander-customer-transaction-prediction"