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fix: fix a bug in cross validation (#618)

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* fix a bug in cross_validation

* fix cross-validation prompt position

* fix

* fix

* append all error messages and full traceback details in execution

---------

Co-authored-by: Bowen Xian <xianbowen@outlook.com>
This commit is contained in:
Yuante Li
2025-02-20 16:18:40 +08:00
committed by GitHub
parent 433594297a
commit b64ab18caa
5 changed files with 8 additions and 9 deletions
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rdagent/components/coder/data_science/ensemble/prompts.yaml
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@@ -95,7 +95,7 @@ ensemble_eval:
Please respond with your feedback in the following JSON format and order
```json
{
"execution": "Describe how well the ensemble executed, including any errors or issues encountered. Retain all error messages and traceback details.",
"execution": "Describe how well the ensemble executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
"return_checking": "Detail the checks performed on the ensemble results, including shape and value validation.",
"code": "Assess code quality, readability, and adherence to specifications.",
"final_decision": <true/false>
rdagent/components/coder/data_science/feature/prompts.yaml
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@@ -98,7 +98,7 @@ feature_eval:
Please respond with your feedback in the following JSON format and order
```json
{
"execution": "Describe how well the feature engineering executed, including any errors or issues encountered. Retain all error messages and traceback details.",
"execution": "Describe how well the feature engineering executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
"return_checking": "Evaluate the correctness and integrity of processed data, checking for missing values, incorrect transformations, and data consistency.",
"code": "Assess code quality, readability, and adherence to specifications. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for optimization.",
"final_decision": <true/false>
rdagent/components/coder/data_science/model/prompts.yaml
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@@ -123,7 +123,7 @@ model_eval:
Please respond with your feedback in the following JSON format and order
```json
{
"execution": "Describe how well the model building executed, including any errors or issues encountered. Retain all error messages and traceback details.",
"execution": "Describe how well the model building executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
"return_checking": "Check the generated value, including whether the value is generated and comparing the shape of the model output with the requirement in spec.md. You also need to check whether the hyperparameters used for retraining are correctly returned during the test execution of the model.",
"code": "Assess code quality, readability, and adherence to specifications. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for optimization.",
"final_decision": <true/false>
rdagent/components/coder/data_science/raw_data_loader/prompts.yaml
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@@ -259,7 +259,7 @@ spec:
3. Dataset Splitting
- The dataset returned by `load_data` is not split into training and testing sets, so the dataset splitting should happen after calling `feat_eng`.
- Choose the splitting methods you deem useful and appropriate based on the `Competition Information`.
- Decide whether to use a **static train-test split** or **cross-validation**, based on what is most suitable given the `Competition Information`.
4. Submission File:
- Save the final predictions as `submission.csv`, ensuring the format matches the competition requirements (refer to `sample_submission` in the Folder Description for the correct structure).
@@ -396,7 +396,7 @@ data_loader_eval:
Your response must follow this structured JSON format:
```json
{
"execution": "Describe how well the data loader executed, including any errors or issues encountered. Retain all error messages and traceback details.",
"execution": "Describe how well the data loader executed, including any errors or issues encountered. Append all error messages and full traceback details without summarizing or omitting any information.",
"return_checking": "Evaluate the correctness and integrity of the loaded data. Check for issues like missing values, incorrect data types, outliers, or formatting inconsistencies.",
"code": "Assess code quality, readability, and adherence to best practices. Consider efficiency, including whether the code utilizes multi-threading or GPU acceleration for faster data loading.",
"final_decision": <true/false>
rdagent/components/coder/data_science/workflow/prompts.yaml
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@@ -38,9 +38,8 @@ workflow_coder:
- Each functionality is in a separate Python file.
2. Your task is only to integrate the existing processes of load_data, feature, model, and ensemble into a complete workflow. Do not edit or modify the existing Python files. The final step should output the predictions in the required format.
3. The user may provide specific code organization rules and instructions. Ensure that the integration follows the given framework and structure.
4. Implement cross-validation when training models to evaluate performance on different subsets of the data if you think it is necessary.
5. After predicting the output, print the shape and other information of the output to stdout to help the evaluator assess the code.
6. You should avoid using logging module to output information in your generated code, and instead use the print() function.
4. After predicting the output, print the shape and other information of the output to stdout to help the evaluator assess the code.
5. You should avoid using logging module to output information in your generated code, and instead use the print() function.
## Output Format
Please response the code in the following json format. Here is an example structure for the JSON output:
@@ -117,7 +116,7 @@ workflow_eval:
Please respond with your feedback in the following JSON format and order
```json
{
"execution": "Describe whether the main workflow executed successfully, correctly integrating all components and generating the final submission. Include any errors or issues encountered, and retain all error messages and traceback details.",
"execution": "Describe whether the main workflow executed successfully, correctly integrating all components and generating the final submission. Include any errors or issues encountered, and append all error messages and full traceback details without summarizing or omitting any information.",
"return_checking": "Verify the generated files, particularly the submission file. Ensure that its format matches the sample submission, checking the index, column names, and CSV content.",
"code": "Provide feedback on code quality, readability, and adherence to the given specifications.",
"final_decision": <true/false>