refine prompt

metagpt/prompts/ml_engineer.py

@@ -174,11 +174,29 @@ Write complete code for 'Current Task'. And avoid duplicating code from 'Done Ta
 Specifically, {special_prompt}
 
 # Code Steps:
-Follow steps below when you writing code if it's convenient.
+Strictly follow steps below when you writing code if it's convenient.
 {code_steps}
 
+# Output Example:
+when current task is "train a lightgbm model on training data", and their are two steps in 'Code Steps', the code be like:
+```python
+# Step 1: check data type and convert to numeric
+ojb_cols = train.select_dtypes(include='object').columns.tolist()
+
+for col in obj_cols:
+    encoder = LabelEncoder()
+    train[col] = encoder.fit_transform(train[col])
+    test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')
+    test[col] = encoder.transform(test[col])
+
+# Step 2: train lightgbm model
+model = LGBMClassifier()
+model.fit(train, y_train)
+```end
+
 # Constraints:
 - Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.
+- The output code should contain all steps implemented in 'Code Steps'.
 """
 
 TOOL_USAGE_PROMPT = """
@@ -202,7 +220,7 @@ Write complete code for 'Current Task'. And avoid duplicating code from 'Done Ta
 Specifically, {special_prompt}
 
 # Code Steps:
-Follow steps below when you writing code if it's convenient.
+Strictly follow steps below when you writing code if it's convenient.
 {code_steps}
 
 # Capabilities
@@ -214,8 +232,9 @@ Each Class tool is described in JSON format. When you call a tool, import the to
 {tool_catalog}
 
 # Output Example:
-when current task is "fill missing value and handle outliers", and their are training data and test data, the output code be like:
+when current task is "do data preprocess, like fill missing value, handle outliers, etc.", and their are two steps in 'Code Steps', the code be like:
 ```python
+# Step 1: fill missing value
 # Tools used: ['FillMissingValue']
 from metagpt.tools.functions.libs.data_preprocess import FillMissingValue
 
@@ -227,6 +246,7 @@ fill_missing_value.fit(train_processed)
 train_processed = fill_missing_value.transform(train_processed)
 test_processed = fill_missing_value.transform(test_processed)
 
+# Step 2: handle outliers
 for col in num_cols:
     low, high = train_processed[col].quantile([0.01, 0.99])
     train_processed[col] = train_processed[col].clip(low, high)
@@ -235,8 +255,9 @@ for col in num_cols:
 
 # Constraints:
 - Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.
-- Prioritize using pre-defined tools for the same functionality.
+- Always prioritize using pre-defined tools for the same functionality.
 - Always copy the DataFrame before processing it and use the copy to process.
+- The output code should contain all steps implemented correctly in 'Code Steps'.
 """
 #- If 'Code Steps' contains step done in 'Done Tasks', such as reading data, don't repeat it.
 
@@ -266,7 +287,7 @@ The current task is about training a model, please ensure high performance:
 
 MODEL_EVALUATE_PROMPT = """
 The current task is about evaluating a model, please note the following:
-- Ensure that the evaluated data is same processed as the training data.
+- Ensure that the evaluated data is same processed as the training data. If not, remember use object in 'Done Tasks' to transform the data.
 - Use trained model from previous task result directly, do not mock or reload model yourself.
 """
 

metagpt/roles/ml_engineer.py

@@ -32,13 +32,6 @@ STRUCTURAL_CONTEXT = """
 {tasks}
 ## Current Task
 {current_task}
-## Packages Installed
-scikit-learn
-pandas
-numpy
-lightgbm
-xgboost
-catboost
 """
 
 
@@ -212,7 +205,7 @@ class MLEngineer(Role):
             result, success = await self.execute_code.run(code)
             # truncated the result
             print(truncate(result))
-            
+
             self.working_memory.add(
                 Message(content=truncate(remove_escape_and_color_codes(result)), role="user", cause_by=ExecutePyCode)
             )