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feat: update prompts and descriptions for data science components (#731)

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* docs: Update prompts and descriptions for data science components

* chore: Remove outdated comments from conf.py

* feat: Add metric_name attribute to DataScienceScen class

* style: Update description in prompts.yaml and reorder metric_name init

* docs: Update prompts.yaml with feature engineering guidelines
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you-n-g
2025-04-01 20:54:42 +08:00
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commit cc063dd646
7 changed files with 21 additions and 14 deletions
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rdagent/components/coder/data_science/raw_data_loader/prompts.yaml
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@@ -109,10 +109,11 @@ spec:
2. Precautions for Feature Engineering:
- Well handle the shape of the data:
- The sample size of the train data and the test data should be the same in all scenarios.
- To most of the scenario, the input shape and the output shape should be exactly the same.
- To some tabular data, you may add or remove some columns so your inferred column number may be unsure.
- To some tabular or time-series data, you may add or remove some columns so your inferred column number may be unsure.
- For scenarios where each dimension does not have a special meaning (like image, audio, and so on), the input shape and the output shape should be exactly the same in most cases unless there is a compelling reason to change them.
- Integration with the Model Pipeline:
- If feature engineering is deferred to the model pipeline for better overall performance, state explicitly that it will be handled at the model stage.
- Model-related operations should not be implemented in this step. (e.g., it uses tools combined with models like torch.Dataset with rich data transformation/augmentation)
- Otherwise, ensure this function applies all required transformations while avoiding data leakage.
- General Considerations:
- Ensure scalability for large datasets.
@@ -174,6 +175,7 @@ spec:
4. Notes:
- Align `DT` (data type) with the definitions used in Feature Engineering specifications.
- The device has GPU support, so you are encouraged to use it for training if necessary to accelerate the process.
- Some data transformations/augmentations can be included in this step (e.g., data tools provided by TensorFlow and Torch)
{% if latest_spec %}
5. Former Specification: