define prompt for ml_engineer

metagpt/prompts/ml_engineer.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Time    : 2023/11/24 15:43
+# @Author  : lidanyang
+# @File    : ml_engineer
+# @Desc    :
+TOOL_RECOMMENDATION_PROMPT = """
+## Comprehensive Task Description:
+{task}
+
+This task is divided into several steps, and you need to select the most suitable tools for each step. A tool means a function that can be used to help you solve the task.
+
+## Detailed Code Steps for the Task:
+{code_steps}
+
+## List of Available Tools:
+{available_tools}
+
+## Tool Selection and Instructions:
+- For each code step listed above, choose up to five tools that are most likely to be useful in solving the task.
+- If you believe that no tools are suitable for a step, indicate with an empty list.
+- Only list the names of the tools, not the full schema of each tool.
+- The result should only contain tool names that are in the list of available tools.
+- The result list should be in the same order as the code steps.
+"""
+
+SELECT_FUNCTION_TOOLS = {
+    "name": "select_function_tools",
+    "description": "Given code steps to generate full code for a task, select suitable tools for each step by order.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "recommend_tools": {
+                "type": "array",
+                "description": "List of tool names for each code step. Empty list if no tool is suitable.",
+                "items": {
+                    "type": "array",
+                    "items": {
+                        "type": "string",
+                    },
+                },
+            },
+        },
+        "required": ["recommend_tools"],
+    },
+}
+
+
+CODE_GENERATOR_WITH_TOOLS = {
+    "name": "add_subtask_code",
+    "description": "Add new code of current subtask to the end of an active Jupyter notebook.",
+    "parameters": {
+        "type": "object",
+        "properties": {
+            "code": {
+                "type": "string",
+                "description": "The code to be added.",
+            },
+        },
+        "required": ["code"],
+    },
+}
+
+TOO_ORGANIZATION_PROMPT = """
+As a senior data scientist, your role involves developing code for a specific sub-task within a larger project. This project is divided into several sub-tasks, which may either be new challenges or extensions of previous work.
+
+## Sub-tasks Overview
+Here's a list of all the sub-tasks, indicating their current status (DONE or TODO). Your responsibility is the first TODO task on this list.
+{all_tasks}
+
+## Historical Code (Previously Done Sub-tasks):
+This code, already executed in the Jupyter notebook, is critical for understanding the background and foundation for your current task.
+```python
+{completed_code}
+```
+
+## Dataset Description:
+Details about the dataset for the project:
+{data_desc}
+
+## Current Task Notion:
+{special_prompt}
+
+## Code Steps for Your Sub-task:
+Follow these steps to complete your current TODO task. You may use external Python functions or write custom code as needed. Ensure your code is self-contained.
+{code_steps}
+
+When you call a function, you should import the function from `{module_name}` first, e.g.:
+```python
+from metagpt.tools.functions.libs.feature_engineering import fill_missing_value
+```
+
+## Available Functions for Each Step:
+Each function is described in JSON format, including the function name and parameters. {output_desc}
+{available_tools}
+
+## Your Output Format:
+Generate the complete code for every step, listing any used function tools at the beginning of the step:
+```python
+# Step 1
+# Tools used: [function names or 'none']
+<your code for this step, without any comments>
+
+# Step 2
+# Tools used: [function names or 'none']
+<your code for this step, without any comments>
+
+# Continue with additional steps, following the same format...
+```end
+
+*** Important Rules ***
+- Use only the tools designated for each code step.
+- Your output should only include code for the current sub-task. Don't repeat historical code.
+- Only mention functions in comments if used in the code.
+- Ensure the output new code is executable in the current Jupyter notebook environment, with all historical code executed.
+"""
+
+
+DATA_PREPROCESS_PROMPT = """
+In data preprocessing, closely monitor each column's data type. Apply suitable methods for various types (numerical, categorical, datetime, textual, etc.) to ensure the pandas.DataFrame is correctly formatted.
+Additionally, ensure that the columns being processed must be the ones that actually exist in the dataset.
+"""
+
+FEATURE_ENGINEERING_PROMPT = """
+"""
+
+CLASSIFICATION_MODEL_PROMPT = """
+"""
+
+REGRESSION_MODEL_PROMPT = """
+"""
+
+
+DATA_PREPROCESS_OUTPUT_DESC = "Please note that all functions uniformly output a processed pandas.DataFrame, facilitating seamless integration into the broader workflow."
+
+FEATURE_ENGINEERING_OUTPUT_DESC = ""
+
+CLASSIFICATION_MODEL_OUTPUT_DESC = ""
+
+REGRESSION_MODEL_OUTPUT_DESC = ""
+
+
+ML_SPECIFIC_PROMPT = {
+    "data_preprocess": DATA_PREPROCESS_PROMPT,
+    "feature_engineering": FEATURE_ENGINEERING_PROMPT,
+    "classification_model": CLASSIFICATION_MODEL_PROMPT,
+    "regression_model": REGRESSION_MODEL_PROMPT,
+}
+
+TOOL_OUTPUT_DESC = {
+    "data_preprocess": DATA_PREPROCESS_OUTPUT_DESC,
+    "feature_engineering": FEATURE_ENGINEERING_OUTPUT_DESC,
+    "classification_model": CLASSIFICATION_MODEL_OUTPUT_DESC,
+    "regression_model": REGRESSION_MODEL_OUTPUT_DESC,
+}
+
+ML_MODULE_MAP = {
+    "data_preprocess": "metagpt.tools.functions.libs.machine_learning.data_preprocess",
+    "feature_engineering": "metagpt.tools.functions.libs.machine_learning.feature_engineering",
+    "classification_model": "metagpt.tools.functions.libs.machine_learning.ml_model",
+    "regression_model": "metagpt.tools.functions.libs.machine_learning.ml_model",
+}