python构建lightgbm模型，制作force plot代码

构建LightGBM模型可以使用以下代码：

import lightgbm as lgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split

# 载入数据
data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# 构建LightGBM模型
params = {
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': {'l2', 'l1'},
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
    'bagging_fraction': 0.8,
    'bagging_freq': 5,
    'verbose': 0
}

lgb_train = lgb.Dataset(X_train, y_train)
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)

gbm = lgb.train(params,
                lgb_train,
                num_boost_round=100,
                valid_sets=lgb_eval,
                early_stopping_rounds=5)

# 预测结果
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

制作Force Plot需要使用shap包，可以使用以下代码实现：

import shap

# 计算SHAP值
explainer = shap.TreeExplainer(gbm)
shap_values = explainer.shap_values(X_test)

# 制作Force Plot
shap.initjs()
shap.force_plot(explainer.expected_value, shap_values[0,:], X_test[0,:])

其中，`shap.TreeExplainer()`用于计算SHAP值，`shap_values()`用于计算所有测试数据的SHAP值，`shap.force_plot()`用于制作Force Plot。