[AI学习笔记]DeepSeek 在金融风控中的特征工程实践

# 示例代码：数据预处理
import pandas as pd
from deepseek.feature_store import FeatureStore

# 初始化特征存储
fs = FeatureStore()

# 加载原始数据
transaction_data = pd.read_csv('transaction_data.csv')
user_data = pd.read_csv('user_data.csv')

# 数据清洗
transaction_data = transaction_data.dropna(subset=['amount', 'timestamp'])
transaction_data['timestamp'] = pd.to_datetime(transaction_data['timestamp'])

# 特征对齐
merged_data = pd.merge(transaction_data, user_data, on='user_id')

# 保存清洗后的数据
fs.save('cleaned_transaction_data', merged_data)

# 示例代码：特征生成
from deepseek.features import FeatureGenerator

# 初始化特征生成器
fg = FeatureGenerator()

# 生成统计特征
stat_features = fg.generate_statistical_features(merged_data, 
                                                groupby='user_id', 
                                                columns=['amount', 'frequency'])

# 生成时间序列特征
time_features = fg.generate_time_series_features(merged_data, 
                                                time_column='timestamp', 
                                                windows=[7, 30, 90])

# 生成图特征
graph_features = fg.generate_graph_features(merged_data, 
                                           entity_column='user_id', 
                                           relation_column='merchant_id')

# 合并特征
final_features = pd.concat([stat_features, time_features, graph_features], axis=1)

# 特征选择
selected_features = fg.select_features(final_features, target='is_fraud', method='mutual_info')

# 示例代码：特征存储与管理
from deepseek.feature_store import FeatureRegistry

# 初始化特征注册表
fr = FeatureRegistry()

# 注册特征
fr.register_features(selected_features, 
                    name='fraud_detection_features', 
                    description='Features for credit card fraud detection',
                    tags=['fraud', 'credit_card'])

# 查询特征
registered_features = fr.get_features('fraud_detection_features', version='1.0')

# 特征血缘图
lineage_graph = fr.get_lineage('fraud_detection_features')
lineage_graph.visualize()

# 示例代码：特征监控
from deepseek.monitoring import FeatureMonitor

# 初始化特征监控器
fm = FeatureMonitor()

# 添加监控指标
fm.add_drift_detector('amount_mean', method='ks_test', threshold=0.05)
fm.add_performance_metric('amount_mean', metric='roc_auc', target='is_fraud')

# 运行监控
monitoring_results = fm.run(selected_features)

# 可视化监控结果
fm.plot_drift('amount_mean')
fm.plot_performance('amount_mean')

# 步骤 1：拉取 DeepSeek 镜像
docker pull deepseek/feature-platform:latest

# 步骤 2：配置环境变量
echo "DEEPSEEK_FEATURE_STORE_URI=feature-store.s3.amazonaws.com" > .env
echo "DEEPSEEK_MONITORING_ENABLED=True" >> .env

# 步骤 3：启动容器
docker-compose up -d

# 示例代码：工作流定义
from deepseek.workflow import Workflow

# 定义工作流
wf = Workflow(name='fraud_detection_workflow')

# 添加步骤
wf.add_step('data_collection', 
           image='deepseek/data-collector', 
           params={'sources': ['transaction', 'user']})

wf.add_step('data_processing', 
           image='deepseek/data-processor', 
           params={'cleaning_rules': 'fraud_rules.json'},
           depends_on='data_collection')

wf.add_step('feature_engineering', 
           image='deepseek/feature-generator', 
           params={'feature_config': 'fraud_features.yaml'},
           depends_on='data_processing')

wf.add_step('feature_monitoring', 
           image='deepseek/monitor', 
           params={'metrics': ['drift', 'performance']},
           depends_on='feature_engineering')

# 部署工作流
wf.deploy()

# 示例代码：模型训练与特征绑定
from deepseek.ml import ModelTrainer

# 初始化模型训练器
mt = ModelTrainer()

# 加载特征
features = fr.get_features('fraud_detection_features', version='1.0')

# 定义模型
model = mt.define_model('xgboost', 
                       params={'n_estimators': 100, 'max_depth': 5})

# 绑定特征到模型
mt.bind_features(features)

# 训练模型
training_metrics = mt.train(target='is_fraud', 
                           validation_split=0.2)

# 保存模型
mt.save('fraud_detection_model', version='1.0')

# 示例代码：实时特征服务
from deepseek.serving import FeatureService

# 初始化特征服务
fs = FeatureService()

# 配置服务
fs.configure(features='fraud_detection_features', 
            window_size='5m', 
            cache_ttl='30s')

# 启动服务
fs.start(port=5000)

# 调用服务（示例请求）
{
  "user_id": "12345",
  "transaction_amount": 500.0,
  "merchant_id": "M6789"
}