借助LLM实现模型选择和试验自动化原创

51CTO内容精选

发布于 2024-11-7 07:59

浏览

0收藏

大语言模型（LLM）已成为一种工具，从回答问题到生成任务列表，它们在许多方面简化了我们的工作。如今个人和企业已经使用LLM来帮助完成工作。

代码生成和评估最近已经成为许多商业产品提供的重要功能，以帮助开发人员处理代码。LLM还可以进一步用于处理数据科学工作，尤其是模型选择和试验。

本文将探讨如何将自动化用于模型选择和试验。

借助LLM实现模型选择和试验自动化

我们将设置用于模型训练的数据集和用于自动化的代码。在这个例子中，我们将使用来自Kaggle的信用汽车欺诈数据集。以下是我为预处理过程所做的准备。

import pandas as pd

df = pd.read_csv('fraud_data.csv')
df = df.drop(['trans_date_trans_time', 'merchant', 'dob', 'trans_num', 'merch_lat', 'merch_long'], axis =1)

df = df.dropna().reset_index(drop = True)
df.to_csv('fraud_data.csv', index = False)

我们将只使用一些数据集，丢弃所有缺失的数据。这不是最优的过程，但我们关注的是模型选择和试验。

接下来，我们将为我们的项目准备一个文件夹，将所有相关文件放在那里。首先，我们将为环境创建requirements.txt文件。你可以用下面的软件包来填充它们。

openai
pandas
scikit-learn
pyyaml

接下来，我们将为所有相关的元数据使用YAML文件。这将包括OpenAI API密钥、要测试的模型、评估度量指标和数据集的位置。

llm_api_key: "YOUR-OPENAI-API-KEY"
default_models:
  - LogisticRegression
  - DecisionTreeClassifier
  - RandomForestClassifier
metrics: ["accuracy", "precision", "recall", "f1_score"]
dataset_path: "fraud_data.csv"

然后，我们导入这个过程中使用的软件包。我们将依靠Scikit-Learn用于建模过程，并使用OpenAI的GPT-4作为LLM。

import pandas as pd
import yaml
import ast
import re
import sklearn
from openai import OpenAI
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

此外，我们将设置辅助（helper）函数和信息来帮助该过程。从数据集加载到数据预处理，配置加载器在如下的函数中。

model_mapping = {
    "LogisticRegression": LogisticRegression,
    "DecisionTreeClassifier": DecisionTreeClassifier,
    "RandomForestClassifier": RandomForestClassifier
}

def load_config(config_path='config.yaml'):
    with open(config_path, 'r') as file:
        config = yaml.safe_load(file)
    return config

def load_data(dataset_path):
    return pd.read_csv(dataset_path)

def preprocess_data(df):
    label_encoders = {}
    for column in df.select_dtypes(include=['object']).columns:
        le = LabelEncoder()
        df[column] = le.fit_transform(df[column])
        label_encoders[column] = le
    return df, label_encoders

在同一个文件中，我们将LLM设置为扮演机器学习角色的专家。我们将使用下面的代码来启动它。

def call_llm(prompt, api_key):
    client = OpenAI(api_key=api_key)
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are an expert in machine learning and able to evaluate the model well."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content.strip()

你可以将LLM模型更改为所需的模型，比如来自Hugging Face的开源模型，但我们建议暂且坚持使用OpenAI。

我将在下面的代码中准备一个函数来清理LLM结果。这确保了输出可以用于模型选择和试验步骤的后续过程。

def clean_hyperparameter_suggestion(suggestion):
    pattern = r'\{.*?\}'
    match = re.search(pattern, suggestion, re.DOTALL)
    if match:
        cleaned_suggestion = match.group(0)
        return cleaned_suggestion
    else:
        print("Could not find a dictionary in the hyperparameter suggestion.")
        return None

def extract_model_name(llm_response, available_models):
    for model in available_models:
        pattern = r'\b' + re.escape(model) + r'\b'
        if re.search(pattern, llm_response, re.IGNORECASE):
            return model
    return None

def validate_hyperparameters(model_class, hyperparameters):
    valid_params = model_class().get_params()
    invalid_params = []
    for param, value in hyperparameters.items():
        if param not in valid_params:
            invalid_params.append(param)
        else:
            if param == 'max_features' and value == 'auto':
                print(f"Invalid value for parameter '{param}': '{value}'")
                invalid_params.append(param)
    if invalid_params:
        print(f"Invalid hyperparameters for {model_class.__name__}: {invalid_params}")
        return False
    return True

def correct_hyperparameters(hyperparameters, model_name):
    corrected = False
    if model_name == "RandomForestClassifier":
        if 'max_features' in hyperparameters and hyperparameters['max_features'] == 'auto':
            print("Correcting 'max_features' from 'auto' to 'sqrt' for RandomForestClassifier.")
            hyperparameters['max_features'] = 'sqrt'
            corrected = True
    return hyperparameters, corrected

然后，我们将需要该函数来启动模型和评估训练过程。下面的代码将用于通过接受分割器数据集、我们要映射的模型名称以及超参数来训练模型。结果将是度量指标和模型对象。

def train_and_evaluate(X_train, X_test, y_train, y_test, model_name, hyperparameters=None):
    if model_name not in model_mapping:
        print(f"Valid model names are: {list(model_mapping.keys())}")
        return None, None

    model_class = model_mapping.get(model_name)
    try:
        if hyperparameters:
            hyperparameters, corrected = correct_hyperparameters(hyperparameters, model_name)
            if not validate_hyperparameters(model_class, hyperparameters):
                return None, None
            model = model_class(**hyperparameters)
        else:
            model = model_class()
    except Exception as e:
        print(f"Error instantiating model with hyperparameters: {e}")
        return None, None
    try:
        model.fit(X_train, y_train)
    except Exception as e:
        print(f"Error during model fitting: {e}")
        return None, None


    y_pred = model.predict(X_test)
    metrics = {
        "accuracy": accuracy_score(y_test, y_pred),
        "precision": precision_score(y_test, y_pred, average='weighted', zero_division=0),
        "recall": recall_score(y_test, y_pred, average='weighted', zero_division=0),
        "f1_score": f1_score(y_test, y_pred, average='weighted', zero_division=0)
    }
    return metrics, model

准备就绪后，我们就可以设置自动化过程了。有几个步骤我们可以实现自动化，其中包括：

1.训练和评估所有模型

2. LLM选择最佳模型

3. 检查最佳模型的超参数调优

4. 如果LLM建议，自动运行超参数调优

def run_llm_based_model_selection_experiment(df, config):
    #Model Training
    X = df.drop("is_fraud", axis=1)
    y = df["is_fraud"]
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

    available_models = config['default_models']
    model_performance = {}

    for model_name in available_models:
        print(f"Training model: {model_name}")
        metrics, _ = train_and_evaluate(X_train, X_test, y_train, y_test, model_name)
        model_performance[model_name] = metrics
        print(f"Model: {model_name} | Metrics: {metrics}")

    #LLM selecting the best model
    sklearn_version = sklearn.__version__
    prompt = (
        f"I have trained the following models with these metrics: {model_performance}. "
        "Which model should I select based on the best performance?"
    )
    best_model_response = call_llm(prompt, config['llm_api_key'])
    print(f"LLM response for best model selection:\n{best_model_response}")

    best_model = extract_model_name(best_model_response, available_models)
    if not best_model:
        print("Error: Could not extract a valid model name from LLM response.")
        return
    print(f"LLM selected the best model: {best_model}")

    #Check for hyperparameter tuning
    prompt_tuning = (
        f"The selected model is {best_model}. Can you suggest hyperparameters for better performance? "
        "Please provide them in Python dictionary format, like {'max_depth': 5, 'min_samples_split': 4}. "
        f"Ensure that all suggested hyperparameters are valid for scikit-learn version {sklearn_version}, "
        "and avoid using deprecated or invalid values such as 'max_features': 'auto'. "
        "Don't provide any explanation or return in any other format."
    )
    tuning_suggestion = call_llm(prompt_tuning, config['llm_api_key'])
    print(f"Hyperparameter tuning suggestion received:\n{tuning_suggestion}")

    cleaned_suggestion = clean_hyperparameter_suggestion(tuning_suggestion)
    if cleaned_suggestion is None:
        suggested_params = None
    else:
        try:
            suggested_params = ast.literal_eval(cleaned_suggestion)
            if not isinstance(suggested_params, dict):
                print("Hyperparameter suggestion is not a valid dictionary.")
                suggested_params = None
        except (ValueError, SyntaxError) as e:
            print(f"Error parsing hyperparameter suggestion: {e}")
            suggested_params = None

    #Automatically run hyperparameter tuning if suggested
    if suggested_params:
        print(f"Running {best_model} with suggested hyperparameters: {suggested_params}")
        tuned_metrics, _ = train_and_evaluate(
            X_train, X_test, y_train, y_test, best_model, hyperparameters=suggested_params
        )
        print(f"Metrics after tuning: {tuned_metrics}")
    else:
        print("No valid hyperparameters were provided for tuning.")

在上面的代码中，我指定了LLM如何根据试验评估我们的每个模型。我们使用以下提示根据模型的性能来选择要使用的模型。

prompt = (
        f"I have trained the following models with these metrics: {model_performance}. "
        "Which model should I select based on the best performance?")

你始终可以更改提示，以实现模型选择的不同规则。

一旦选择了最佳模型，我将使用以下提示来建议应该使用哪些超参数用于后续过程。我还指定了Scikit-Learn版本，因为超参数因版本的不同而有变化。

prompt_tuning = (
        f"The selected model is {best_model}. Can you suggest hyperparameters for better performance? "
        "Please provide them in Python dictionary format, like {'max_depth': 5, 'min_samples_split': 4}. "
        f"Ensure that all suggested hyperparameters are valid for scikit-learn version {sklearn_version}, "
        "and avoid using deprecated or invalid values such as 'max_features': 'auto'. "
        "Don't provide any explanation or return in any other format.")

你可以以任何想要的方式更改提示，比如通过更大胆地尝试调优超参数，或添加另一种技术。

我把上面的所有代码放在一个名为automated_model_llm.py的文件中。最后，添加以下代码以运行整个过程。

def main():
    config = load_config()
    df = load_data(config['dataset_path'])
    df, _ = preprocess_data(df)
    run_llm_based_model_selection_experiment(df, config)


if __name__ == "__main__":
    main()

一旦一切准备就绪，你就可以运行以下代码来执行代码。

python automated_model_llm.py

输出：

LLM selected the best model: RandomForestClassifier
Hyperparameter tuning suggestion received:
{
'n_estimators': 100,
'max_depth': None,
'min_samples_split': 2,
'min_samples_leaf': 1,
'max_features': 'sqrt',
'bootstrap': True
}
Running RandomForestClassifier with suggested hyperparameters: {'n_estimators': 100, 'max_depth': None, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'bootstrap': True}
Metrics after tuning: {'accuracy': 0.9730041532071989, 'precision': 0.9722907483489197, 'recall': 0.9730041532071989, 'f1_score': 0.9724045530119824}

这是我试验得到的示例输出。它可能和你的不一样。你可以设置提示和生成参数，以获得更加多变或严格的LLM输出。然而，如果你正确构建了代码的结构，可以将LLM运用于模型选择和试验自动化。