Model Interpretation
from ngboost import NGBClassifier, NGBRegressor
from ngboost.distns import k_categorical, Normal
from ngboost.scores import LogScore
from sklearn.datasets import load_breast_cancer, load_boston
from sklearn.model_selection import train_test_split
X, Y = load_boston(True)
X_reg_train, X_reg_test, Y_reg_train, Y_reg_test = train_test_split(X, Y, test_size=0.2)
X, y = load_breast_cancer(True)
y[0:15] = 2 # artificially make this a 3-class problem instead of a 2-class problem
X_cls_train, X_cls_test, Y_cls_train, Y_cls_test = train_test_split(X, y, test_size=0.2)
我们提醒用户不要试图从任何机器学习模型中过度解读，但NGBoost确实提供了方法来解释使用回归树基础学习器拟合的模型。由于分布中的每个参数都是由单独的学习器序列拟合的，因此将会有多个模型解释结果，每个参数一个。默认使用的分布是Normal，因此以下示例显示了loc和scale参数的结果。
ngb = NGBRegressor(verbose=False).fit(X_reg_train, Y_reg_train)
## Feature importance for loc trees
feature_importance_loc = ngb.feature_importances_[0]
## Feature importance for scale trees
feature_importance_scale = ngb.feature_importances_[1]
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
df_loc = pd.DataFrame({'feature':load_boston()['feature_names'],
'importance':feature_importance_loc})\
.sort_values('importance',ascending=False)
df_scale = pd.DataFrame({'feature':load_boston()['feature_names'],
'importance':feature_importance_scale})\
.sort_values('importance',ascending=False)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,6))
fig.suptitle("Feature importance plot for distribution parameters", fontsize=17)
sns.barplot(x='importance',y='feature',ax=ax1,data=df_loc, color="skyblue").set_title('loc param')
sns.barplot(x='importance',y='feature',ax=ax2,data=df_scale, color="skyblue").set_title('scale param')
import shap
shap.initjs()
## SHAP plot for loc trees
explainer = shap.TreeExplainer(ngb, model_output=0) # use model_output = 1 for scale trees
shap_values = explainer.shap_values(X_reg_train)
shap.summary_plot(shap_values, X_reg_train, feature_names=load_boston()['feature_names'])
