MetaGPT使用工具进行机器学习建模
工具批量使用
任务
使用 DataInterpreter 对 titanic 数据集进行建模预测，并在数据预处理和特征工程阶段批量调用工具（已经预先创建了多个 data_preprocess 和 feature_engineering 类型的工具，位于目录metagpt/tools/libs下）。
代码
examples/di/machine_learning_with_tools.py
bash
python examples/di/machine_learning_with_tools.pypython examples/di/machine_learning_with_tools.py运行结果
数据预处理代码（其中包含 3 个工具的使用：FillMissingValue、StandardScale 和 OneHotEncode）如下：
python
from metagpt.tools.libs.data_preprocess import FillMissingValue, StandardScale, OneHotEncode
fill_age = FillMissingValue(features=['Age'], strategy='median')
fill_embarked = FillMissingValue(features=['Embarked'], strategy='most_frequent')
train_data = fill_age.fit_transform(train_data)
train_data = fill_embarked.fit_transform(train_data)
if 'Cabin' in train_data.columns:
train_data.drop('Cabin', axis=1, inplace=True)
numerical_features = ['Age', 'SibSp', 'Parch', 'Fare']
scale = StandardScale(features=numerical_features)
scaled_features = scale.fit_transform(train_data)
for feature in numerical_features:
train_data[feature] = scaled_features[feature]
categorical_features = ['Sex', 'Embarked']
one_hot = OneHotEncode(features=categorical_features)
train_data = one_hot.fit_transform(train_data)
non_informative_columns = ['PassengerId', 'Name', 'Ticket']
train_data.drop(columns=non_informative_columns, axis=1, inplace=True, errors='ignore')
train_data.head()from metagpt.tools.libs.data_preprocess import FillMissingValue, StandardScale, OneHotEncode
fill_age = FillMissingValue(features=['Age'], strategy='median')
fill_embarked = FillMissingValue(features=['Embarked'], strategy='most_frequent')
train_data = fill_age.fit_transform(train_data)
train_data = fill_embarked.fit_transform(train_data)
if 'Cabin' in train_data.columns:
train_data.drop('Cabin', axis=1, inplace=True)
numerical_features = ['Age', 'SibSp', 'Parch', 'Fare']
scale = StandardScale(features=numerical_features)
scaled_features = scale.fit_transform(train_data)
for feature in numerical_features:
train_data[feature] = scaled_features[feature]
categorical_features = ['Sex', 'Embarked']
one_hot = OneHotEncode(features=categorical_features)
train_data = one_hot.fit_transform(train_data)
non_informative_columns = ['PassengerId', 'Name', 'Ticket']
train_data.drop(columns=non_informative_columns, axis=1, inplace=True, errors='ignore')
train_data.head()特征工程代码（其中包含 1 个工具的使用：PolynomialExpansion）如下：
python
from metagpt.tools.libs.feature_engineering import PolynomialExpansion
numeric_features = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
poly_expansion = PolynomialExpansion(cols=numeric_features, label_col='Survived')
train_data = poly_expansion.fit_transform(train_data)
train_data.head()from metagpt.tools.libs.feature_engineering import PolynomialExpansion
numeric_features = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
poly_expansion = PolynomialExpansion(cols=numeric_features, label_col='Survived')
train_data = poly_expansion.fit_transform(train_data)
train_data.head()机制解释
工具创建
在 metagpt.tools.libs 目录下，我们预先定义了两类工具：数据预处理（data_preprocess）和特征工程（feature_engineering）。下面以数据预处理任务为例，给出一个简单工具的完整创建指引，包括工具的定义和注册方法。
- 首先，在
metagpt.tools.libs目录下创建文件data_preprocess.py。 - 在该文件中，您将定义您的数据预处理工具，并利用
@register_tool装饰器一键完成工具的注册。此外，你也可以通过设置tags参数，指定工具的类别。
代码示例：
python
from typing import Literal
import pandas as pd
from sklearn.impute import SimpleImputer
from metagpt.tools.tool_registry import register_tool
TAGS = ["data preprocessing", "machine learning"]
@register_tool(tags=TAGS)
class FillMissingValue:
"""
Completing missing values with simple strategies.
"""
def __init__(
self, features: list, strategy: Literal["mean", "median", "most_frequent", "constant"] = "mean", fill_value=None
):
"""
Initialize self.
Args:
features (list): Columns to be processed.
strategy (Literal["mean", "median", "most_frequent", "constant"], optional): The imputation strategy, notice 'mean' and 'median' can only
be used for numeric features. Defaults to 'mean'.
fill_value (int, optional): Fill_value is used to replace all occurrences of missing_values.
Defaults to None.
"""
self.features = features
self.strategy = strategy
self.fill_value = fill_value
self.si = None
def fit(self, df: pd.DataFrame):
"""
Fit the FillMissingValue model.
Args:
df (pd.DataFrame): The input DataFrame.
"""
if len(self.features) == 0:
return
self.si = SimpleImputer(strategy=self.strategy, fill_value=self.fill_value)
self.si.fit(df[self.features])
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Transform the input DataFrame with the fitted model.
Args:
df (pd.DataFrame): The input DataFrame.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
if len(self.features) == 0:
return df
new_df = df.copy()
new_df[self.features] = self.si.transform(new_df[self.features])
return new_df
def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Fit and transform the input DataFrame.
Args:
df (pd.DataFrame): The input DataFrame.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
self.fit(df)
return self.transform(df)from typing import Literal
import pandas as pd
from sklearn.impute import SimpleImputer
from metagpt.tools.tool_registry import register_tool
TAGS = ["data preprocessing", "machine learning"]
@register_tool(tags=TAGS)
class FillMissingValue:
"""
Completing missing values with simple strategies.
"""
def __init__(
self, features: list, strategy: Literal["mean", "median", "most_frequent", "constant"] = "mean", fill_value=None
):
"""
Initialize self.
Args:
features (list): Columns to be processed.
strategy (Literal["mean", "median", "most_frequent", "constant"], optional): The imputation strategy, notice 'mean' and 'median' can only
be used for numeric features. Defaults to 'mean'.
fill_value (int, optional): Fill_value is used to replace all occurrences of missing_values.
Defaults to None.
"""
self.features = features
self.strategy = strategy
self.fill_value = fill_value
self.si = None
def fit(self, df: pd.DataFrame):
"""
Fit the FillMissingValue model.
Args:
df (pd.DataFrame): The input DataFrame.
"""
if len(self.features) == 0:
return
self.si = SimpleImputer(strategy=self.strategy, fill_value=self.fill_value)
self.si.fit(df[self.features])
def transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Transform the input DataFrame with the fitted model.
Args:
df (pd.DataFrame): The input DataFrame.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
if len(self.features) == 0:
return df
new_df = df.copy()
new_df[self.features] = self.si.transform(new_df[self.features])
return new_df
def fit_transform(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Fit and transform the input DataFrame.
Args:
df (pd.DataFrame): The input DataFrame.
Returns:
pd.DataFrame: The transformed DataFrame.
"""
self.fit(df)
return self.transform(df)在上述示例中，FillMissingValue 类具体实现了缺失值的填充功能，并通过 @register_tool 装饰器注册为数据预处理工具。
注册工具后，系统将自动根据工具函数的注释在 metagpt.tools.schemas 目录下为您生成每个工具生成相应的 schema 文件（如 FillMissingValue.yml），详细描述工具类的结构、方法以及参数类型，确保LLM能够准确理解并使用该工具。
Schema 文件示例（FillMissingValue.yml）：
yaml
type: class
description: Completing missing values with simple strategies.
methods:
__init__:
type: function
description: 'Initialize self. '
signature:
'(self, features: ''list'', strategy: "Literal[''mean'', ''median'',
''most_frequent'', ''constant'']" = ''mean'', fill_value=None)'
parameters:
'Args: features (list): Columns to be processed. strategy (Literal["mean",
"median", "most_frequent", "constant"], optional): The imputation strategy,
notice ''mean'' and ''median'' can only be used for numeric features. Defaults
to ''mean''. fill_value (int, optional): Fill_value is used to replace all occurrences
of missing_values. Defaults to None.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: "(self, df: 'pd.DataFrame')"
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: "(self, df: 'pd.DataFrame') -> 'pd.DataFrame'"
parameters:
'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: "(self, df: 'pd.DataFrame') -> 'pd.DataFrame'"
parameters:
'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'type: class
description: Completing missing values with simple strategies.
methods:
__init__:
type: function
description: 'Initialize self. '
signature:
'(self, features: ''list'', strategy: "Literal[''mean'', ''median'',
''most_frequent'', ''constant'']" = ''mean'', fill_value=None)'
parameters:
'Args: features (list): Columns to be processed. strategy (Literal["mean",
"median", "most_frequent", "constant"], optional): The imputation strategy,
notice ''mean'' and ''median'' can only be used for numeric features. Defaults
to ''mean''. fill_value (int, optional): Fill_value is used to replace all occurrences
of missing_values. Defaults to None.'
fit:
type: function
description: 'Fit a model to be used in subsequent transform. '
signature: "(self, df: 'pd.DataFrame')"
parameters: 'Args: df (pd.DataFrame): The input DataFrame.'
fit_transform:
type: function
description: 'Fit and transform the input DataFrame. '
signature: "(self, df: 'pd.DataFrame') -> 'pd.DataFrame'"
parameters:
'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'
transform:
type: function
description: 'Transform the input DataFrame with the fitted model. '
signature: "(self, df: 'pd.DataFrame') -> 'pd.DataFrame'"
parameters:
'Args: df (pd.DataFrame): The input DataFrame. Returns: pd.DataFrame:
The transformed DataFrame.'自动工具注册与选择
通过设置DataInterpreter的参数tools，可以人为指定使用哪些工具。如果希望使用所有工具，可以设定tools=["<all>"]。
当 DataInterpreter 启动时，它会自动注册 tools 列表中的所有工具。同时，在任务规划阶段，DataInterpreter 会根据需求为每个任务推荐可用的工具，并挑选出 TOP-k 供 LLM 使用。
工具组合与调用
在执行任务时，一旦可用的 TOP 工具被识别，DataInterpreter 会根据任务需求自动组合并调用其中的有用工具，以形成针对特定任务的代码解决方案。