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1. change data.yaml to more generalized path

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2. correct import
This commit is contained in:
Yizhou Chi 2024-08-30 16:59:38 +08:00
parent 211f758b53
commit d14f07f9b1
10 changed files with 76 additions and 56 deletions
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41
expo/utils.py
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@ -1,5 +1,4 @@
import yaml
from examples.MCTS_test.dataset import get_user_requirement, get_split_dataset_path
from metagpt.roles.role import Role
from metagpt.actions.di.execute_nb_code import ExecuteNbCode
from metagpt.utils.save_code import save_code_file
@ -13,34 +12,6 @@ import sys
import os
import re
TASK_PROMPT = """\
# User requirement
{user_requirement}
**Attention** Please do not leak the target label in any form during training.
## Saving Dev and Test Predictions
Save the prediction results of the dev set and test set in `dev_predictions.csv` and `test_predictions.csv` respectively in the output directory BEFORE printig out the results.
The file should contain a single `target` column with the predicted values.
Make sure the prediction results are in the same format as the target column in the training set. The labels should be transformed back to the original format if any transformation was applied during training.
## Output Training Set Performance
Make sure the performance of the model is printed in python in the last step even if it has been printed in the previous steps. The value should be a float number.
Print the training set performance in the last step. Write in this format:
```python
...
print("Train score:", train_score)
```
# Data dir
training: {train_path}
dev: {dev_path}
testing: {test_path}
# Output dir
{output_dir}
"""
def load_data_config(file_path="data.yaml"):
with open(file_path, 'r') as stream:
data_config = yaml.safe_load(stream)
@ -78,18 +49,6 @@ def get_exp_pool_path(task_name, data_config, pool_name="analysis_pool"):
exp_pool_path = os.path.join(data_path, f"{pool_name}.json")
return exp_pool_path
def generate_task_requirement(task_name, data_config):
user_requirement = get_user_requirement(task_name, data_config)
split_dataset_path = get_split_dataset_path(task_name, data_config)
train_path = split_dataset_path["train"]
dev_path = split_dataset_path["dev_wo_target"]
test_path = split_dataset_path["test_wo_target"]
work_dir = data_config["work_dir"]
output_dir = f"{work_dir}/{task_name}"
user_requirement = TASK_PROMPT.format(user_requirement=user_requirement,
train_path=train_path, dev_path=dev_path, test_path=test_path,
output_dir=output_dir)
return user_requirement
def change_plan(role, plan):
print(f"Change next plan to: {plan}")