从 GridSearch CV 中检索 Shapley 值的模型结果答案

【问题标题】：Retrieve Model Results for Shapley values from GridSearch CV从 GridSearch CV 中检索 Shapley 值的模型结果
【发布时间】：2021-10-10 21:54:44
【问题描述】：

我已经使用 GridSearchCV 调整了一个模型。现在我想计算 Shapley 值并将它们可视化。困难在于shap 包不包括模型，而不是 GridSearch 结果。同样，当我将 best_estimator_ 属性传递给它时，它也不喜欢。它说不支持该模型。如何从 GridSearchCV 或其他东西中获取 Shapley 值来计算 Shapley 值。我的专栏之一是分类的，因此需要进行预处理。由于我有来自网格搜索的 best_params，我可以将模型作为 xgboost_regressor 模型运行，但在没有预处理的情况下这样做已经有一段时间了。

from xgboost import XGBRegressor as xgr    
model=xgr(booster ='gbtree', random_state = 13)
cv_inner = KFold(n_splits=5, shuffle=True)
params = {
        'model__n_estimators' : [1500,2000]
         ,'model__learning_rate' : [0.1,0.2,0.3]
         ,'model__gamma' : [0, 0.005,0.01]
         ,'model__lambda' : [0.1, 0.2,0.3]
         ,'model__alpha' : [0, 0.001, 0.05]
         ,'model__max_depth' : [6]
         ,'model__min_child_weight' : [1]
         ,'model__subsample' : [0.8]
    }
preprocessor = ColumnTransformer(
                    transformers=[
                        ('cat', OneHotEncoder(), [0])
                    ]
                    ,remainder = 'passthrough')
mymodel = Pipeline(steps = [
                        ('preprocessor',preprocessor),
                        ('model', model)
                        ])
optimize_hparams = GridSearchCV(
    estimator = mymodel, param_grid=params, n_jobs = -1,
    cv=cv_inner, scoring='neg_mean_absolute_error')
optimize_hparams.fit(X, y)
import shap
shap_values = shap.TreeExplainer(optimize_hparams.best_estimator_['model']).shap_values(X)
shap.summary_plot(shap_values, X, plot_type="bar")

【问题讨论】：

标签： python xgboost grid-search shap

【解决方案1】：

在计算 Shap 值之前，您需要将网格搜索中的预处理器和最佳模型拟合到数据中，示例请参见下面的代码。

import shap
import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import KFold, GridSearchCV
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.pipeline import Pipeline
from xgboost import XGBRegressor as xgr 

# generate the features and target
X, y = make_regression(n_samples=100, n_features=5, random_state=100)

# add a categorical feature in the first column
X = np.hstack([np.random.choice(['a', 'b', 'c'], size=(X.shape[0], 1)), X])

# set up the grid search
model = xgr(booster='gbtree', random_state=13)

cv_inner = KFold(n_splits=5, shuffle=True)

params = {
    'model__n_estimators' : [1500, 2000],
    'model__learning_rate' : [0.1, 0.2, 0.3],
    'model__gamma' : [0, 0.005, 0.01],
    'model__lambda' : [0.1, 0.2, 0.3],
    'model__alpha' : [0, 0.001, 0.05],
    'model__max_depth' : [6],
    'model__min_child_weight' : [1],
    'model__subsample' : [0.8],
}

preprocessor = ColumnTransformer(transformers=[('cat', OneHotEncoder(), [0])], remainder='passthrough')

mymodel = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])

optimize_hparams = GridSearchCV(
    estimator=mymodel, 
    param_grid=params, 
    cv=cv_inner, 
    scoring='neg_mean_absolute_error',
    n_jobs=-1,
)

# run the grid search
optimize_hparams.fit(X, y)

# fit the preprocessor 
X_encoded = optimize_hparams.best_estimator_['preprocessor'].fit_transform(X)

# fit the model 
best_model = optimize_hparams.best_estimator_['model'].fit(X_encoded, y)

# calculate the Shap values
shap_values = shap.TreeExplainer(best_model).shap_values(X_encoded)

# plot the Shap values
shap.summary_plot(shap_values, X_encoded, plot_type='bar')

【讨论】：