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Explain Iris classification by SHAP

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Goal

This post aims to introduce how to explain Iris classification by SHAP.

Reference

Libraries

In [8]:
import sklearn
from sklearn.model_selection import train_test_split
import numpy as np
import shap
import time
shap.initjs()

Load Iris Data

In [2]:
X_train, X_test, Y_train, Y_test = train_test_split(
    *shap.datasets.iris(), test_size=0.2, random_state=0)
In [3]:
# Predictor 
X_train.head()
Out[3]:
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
137 6.4 3.1 5.5 1.8
84 5.4 3.0 4.5 1.5
27 5.2 3.5 1.5 0.2
127 6.1 3.0 4.9 1.8
132 6.4 2.8 5.6 2.2
In [19]:
# Label 
Y_train[:5]
Out[19]:
array([2, 1, 0, 2, 2])

Train K-nearest neighbors

In [4]:
clf = sklearn.neighbors.KNeighborsClassifier()
clf.fit(X_train, Y_train)
Out[4]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=None, n_neighbors=5, p=2,
           weights='uniform')

Create an explainer

In [10]:
explainer = shap.KernelExplainer(clf.predict_proba, X_train)

Using 120 background data samples could cause slower run times. Consider using shap.kmeans(data, K) to summarize the background as K weighted samples.

Use summarized X by k-measn

In [21]:
X_train_summary = shap.kmeans(X_train, 50)
explainer = shap.KernelExplainer(clf.predict_proba, X_train_summary)

Explain one test prediction

In [22]:
shap_values = explainer.shap_values(X_test.iloc[0, :])
shap.force_plot(explainer.expected_value[0], shap_values[0], X_test.iloc[0, :])

/Users/hiro/anaconda3/envs/py367/lib/python3.6/site-packages/shap/explainers/kernel.py:545: UserWarning: l1_reg="auto" is deprecated and in the next version (v0.29) the behavior will change from a conditional use of AIC to simply "num_features(10)"!
  "l1_reg=\"auto\" is deprecated and in the next version (v0.29) the behavior will change from a " \
Out[22]: