Explain Blackbox Classifiers

In this notebook we will use the interpret package to explain blackbox classifiers using SHAP, Lime, MorrisSensitivity, and PartialDependence.

This notebook can be found in our examples folder on GitHub.

# 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)

Train a blackbox classification system

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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Show blackbox model performance

from interpret.perf import ROC

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

Local Explanations: How an individual prediction was made

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)

Global Explanations: How the model behaves overall

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)