feat: refine prompt (#760)

* style: Simplify language and improve clarity in prompts and share.yaml

* style: Update prompt wording for clarity in raw_data_loader

* style: Simplify conditional logic in task_gen system prompt

* refactor: Update prompts and proposal for component output format handling

* fix: Correct grammar and add clarification in prompts.yaml

* feat: Include coding guidelines in data science component prompts

* lint

rdagent/components/coder/data_science/raw_data_loader/prompts.yaml

@@ -224,71 +224,7 @@ spec:
       You should return the specification in markdown format directly, while the **function definition** within it should be in code format, tailored to the Competition Information, with detailed explanations provided in the docstring.
 
     workflow: |-
-      Your task is to implement the main workflow script (`main.py`) for a Kaggle-style machine learning competition project. 
-      Follow the provided project structure and specifications to ensure consistency and maintainability:
-      1. Workflow Integration:
-        - Integrate the following components into the workflow:
-          - Data loading (`load_data.py`).
-          - Feature engineering (`feature.py`).
-          - Model workflow for training and testing (`model_*.py`). 
-          - Ensemble workflow that combines results from the model workflow to obtain the final prediction (`ensemble.py`).
-        - Treat each component as a modular and callable Python function.
-        - The workflow script should be flexible enough to handle either a single model or multiple models, with filenames (model_*.py) that are not determined at the outset.
-          For multiple model selection, utilize Python code to identify eligible models based on filenames, for example:
-          ```python
-          available_models = [f for f in os.listdir('.') if f.startswith('model_') and 'test' not in f]
-          ```
-      2. Feature Engineering
-        - The feature engineering should be called only once. For example:
-          `X_transformed, y_transformed, X_test_transformed = feat_eng(X, y, X_test)`
-        - It should be called before dataset splitting.
-
-      3. Dataset Splitting
-        - The dataset returned by `load_data` is not pre-split. After calling `feat_eng`, split the data into training and test sets.
-        - [Notice] If feasible, apply cross-validation on the training set (`X_transformed`, `y_transformed`) to ensure a reliable assessment of model performance.
-        - Keep the test set (`X_test_transformed`) unchanged, as it is only used for generating the final predictions.
-        - Pseudocode logic for reference:
-          ```
-          Set number of splits and initialize KFold cross-validator.
-          Create dictionaries for validation and test predictions.
-          For each model file:
-              Import the model dynamically.
-              Initialize arrays for out-of-fold (OOF) and test predictions.
-              For each fold in KFold:
-                  Split data into training and validation sets.
-                  Run model workflow to get validation and test predictions.
-                  Validate shapes.
-                  Store validation and test predictions.
-              Compute average test predictions across folds.
-              Save OOF and averaged test predictions.
-          Ensemble predictions from all models and print the final shape.
-          ```
-
-      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).
-        - Present the required submission format explicitly and ensure the output adheres to it.
-
-      5. Code Standards:
-        - Do not use progress bars (e.g., tqdm) in the code.
-
-      6. Ensemble Strategy:
-        Consolidate all model outputs into a dictionary, where each key is the model's filename (excluding the .py extension) and its corresponding value is the model's output.
-        Sample code:
-        {% raw %}
-        {% for model_name in model_names %}
-        model_module = __import__(model_name.replace('.py', ''))
-        val_pred, test_pred, _ = model_module.model_workflow(
-          X=train_X,
-          y=train_y,
-          val_X=val_X,
-          val_y=val_y,
-          test_X=X_test_transformed
-        )
-        val_preds_dict[model_module.__name__] = val_pred
-        test_preds_dict[model_module.__name__] = test_pred
-        {% endfor %}
-        final_pred = ensemble_workflow(test_preds_dict, val_preds_dict, val_y)
-        {% endraw %}
+      {% include "scenarios.data_science.share:component_spec.Workflow" %}
 
       {% if latest_spec %}
       7. Former Specification:
@@ -313,8 +249,8 @@ data_loader_coder:
     {% endif %}
     
     {% if queried_similar_successful_knowledge|length != 0 %}
-    --------- Successful Implementations for Similar Models ---------
-    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Model {{ loop.index }}:=====
+    --------- Successful Implementation Examples for Similar Task ---------
+    ====={% for similar_successful_knowledge in queried_similar_successful_knowledge %} Example {{ loop.index }}:=====
     {{ similar_successful_knowledge.target_task.get_task_information() }}
     =====Code:=====
     {{ similar_successful_knowledge.implementation.all_codes }}
@@ -339,6 +275,7 @@ data_loader_coder:
     from joblib import Memory
     memory = Memory(location='/tmp/cache', verbose=0)
     @memory.cache```
+    {% include "scenarios.data_science.share:guidelines.coding" %}
     
     ## Exploratory Data Analysis (EDA) part(Required):
     - Before returning the data, you should always add an EDA part describing the data to help the following steps understand the data better.