解释黑盒回归模型

在本 notebook 中，我们将使用 interpret 包来使用 SHAP、Lime、MorrisSensitivity 和 PartialDependence 解释黑盒回归模型。

本 notebook 可以在 GitHub 上的 examples 文件夹中找到。

# install interpret if not already installed
try:
    import interpret
except ModuleNotFoundError:
    !pip install --quiet interpret pandas scikit-learn lime

import numpy as np
import pandas as pd
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
from interpret import show

from interpret import set_visualize_provider
from interpret.provider import InlineProvider
set_visualize_provider(InlineProvider())

X, y = load_diabetes(return_X_y=True, as_frame=True)

seed = 42
np.random.seed(seed)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=seed)

训练一个黑盒回归系统

from sklearn.ensemble import RandomForestRegressor
from sklearn.decomposition import PCA
from sklearn.pipeline import Pipeline

#Blackbox system can include preprocessing, not just a regressor!
pca = PCA()
rf = RandomForestRegressor(random_state=seed)

blackbox_model = Pipeline([('pca', pca), ('rf', rf)])
blackbox_model.fit(X_train, y_train)

Pipeline(steps=[('pca', PCA()), ('rf', RandomForestRegressor(random_state=42))])

在 Jupyter 环境中，请重新运行此单元格以显示 HTML 表示或信任该 notebook。
在 GitHub 上，HTML 表示无法渲染，请尝试使用 nbviewer.org 加载此页面。

显示黑盒模型性能

from interpret.perf import RegressionPerf

blackbox_perf = RegressionPerf(blackbox_model).explain_perf(X_test, y_test, name='Blackbox')
show(blackbox_perf)

局部解释：单个预测是如何生成的

from interpret.blackbox import LimeTabular

#Blackbox explainers need a predict function, and optionally a dataset
lime = LimeTabular(blackbox_model, X_train, random_state=1)

#Pick the instances to explain, optionally pass in labels if you have them
lime_local = lime.explain_local(X_test[:5], y_test[:5], name='LIME')

show(lime_local, 0)

from interpret.blackbox import ShapKernel

background_val = pd.DataFrame(np.median(X_train, axis=0).reshape(1, -1), columns=X.columns)
shap = ShapKernel(blackbox_model, background_val)
shap_local = shap.explain_local(X_test[:5], y_test[:5], name='SHAP')
show(shap_local, 0)

全局解释：模型整体行为如何

from interpret.blackbox import MorrisSensitivity

sensitivity = MorrisSensitivity(blackbox_model, X_train)
sensitivity_global = sensitivity.explain_global(name="Global Sensitivity")

show(sensitivity_global)

from interpret.blackbox import PartialDependence

pdp = PartialDependence(blackbox_model, X_train)
pdp_global = pdp.explain_global(name='Partial Dependence')

show(pdp_global, 0)