解释黑盒回归器

在本笔记本中，我们将使用interpret包通过SHAP、Lime、MorrisSensitivity和PartialDependence来解释黑盒回归器。

这个笔记本可以在我们的examples folder在GitHub上找到。

# 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)

训练一个黑箱回归系统

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))])

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显示黑箱模型性能

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：单个预测是如何做出的

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)