解释黑盒分类器

在本 notebook 中，我们将使用 interpret 包使用 SHAP、Lime、MorrisSensitivity 和 PartialDependence 来解释黑盒分类器。

本 notebook 位于 GitHub 上的 示例文件夹 中。

# 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.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())

df = pd.read_csv(
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
    header=None)
df.columns = [
    "Age", "WorkClass", "fnlwgt", "Education", "EducationNum",
    "MaritalStatus", "Occupation", "Relationship", "Race", "Gender",
    "CapitalGain", "CapitalLoss", "HoursPerWeek", "NativeCountry", "Income"
]
X = df.iloc[:, :-1]
y = (df.iloc[:, -1] == " >50K").astype(int)

# We have to transform categorical variables to use sklearn models
X = pd.get_dummies(X, prefix_sep='.').astype(float)

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)

训练一个黑盒分类系统

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

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

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

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

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显示黑盒模型性能

from interpret.perf import ROC

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

局部解释：个体预测是如何做出的

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)

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：模型的整体行为方式

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)