可解释机器学习之SHAP方法

以Breast cancer wisconsin (diagnostic) dataset数据集为例。

# Built-in libraries
import math
import numpy    as np
import pandas   as pd# Visualization libraries
import matplotlib.pyplot as plt
import seaborn           as sns# Sklearn libraries
#
from sklearn                 import metrics
from sklearn                 import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.datasets        import load_breast_cancer

Parameters

test_size = 0.1

Breast cancer wisconsin (diagnostic) dataset

Data Set Characteristics:

:Number of Instances: 569:Number of Attributes: 30 numeric, predictive attributes and the class:Attribute Information:- radius (mean of distances from center to points on the perimeter)- texture (standard deviation of gray-scale values)- perimeter- area- smoothness (local variation in radius lengths)- compactness (perimeter^2 / area - 1.0)- concavity (severity of concave portions of the contour)- concave points (number of concave portions of the contour)- symmetry - fractal dimension ("coastline approximation" - 1)The mean, standard error, and "worst" or largest (mean of the threelargest values) of these features were computed for each image,resulting in 30 features.  For instance, field 3 is Mean Radius, field13 is Radius SE, field 23 is Worst Radius.- class:- WDBC-Malignant- WDBC-Benign:Summary Statistics::Missing Attribute Values: None:Class Distribution: 212 - Malignant, 357 - Benign
# Load Breast Cancer dataset
data = load_breast_cancer() # Create DataFrame
#
df   = pd.DataFrame(data.data, columns=data.feature_names)
# Add a target column
#
df['class'] = data.target# Show DataFrame
df.head(3)

#Pre-processing data
fig = plt.figure(figsize=(8
,3
))ax  = sns.countplot(df['class'], order = df['class'].value_counts().index)#Create annotate
for i in ax.patches:ax.text(x        = i.get_x() + i.get_width()/2, y        = i.get_height()/7, s        = f"{np.round(i.get_height()/len(df)*100)}%", ha       = 'center', size     = 15, weight   = 'bold', rotation = 90, color    = 'white');plt.title("Class variable", size=12, weight='bold');

Training/Testing sets

X = df.iloc[:,:-1]
Y = df.iloc[:, -1]
trainX, testX, trainY, testY = train_test_split(X, Y, test_size=test_size, random_state=42)

Model development

Setup ML model

from sklearn.ensemble import GradientBoostingClassifier# XGBoost model
#
model = GradientBoostingClassifier( random_state  = 42 )

Training ML model

model.fit(trainX, trainY);
model.fit(trainX, trainY);

Get Predictions

# Calculate prediction
#
pred = model.predict( testX )# Performance accuracy
#
accuracy = metrics.accuracy_score(testY, pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
Accuracy: 96.49%

SHAP

%%capture
! pip install shap

import shap
# Generate the Tree explainer and SHAP values
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
expected_value = explainer.expected_value

Explainability/Visualizations

Summary dot plot

# Generate summary dot plot
#
shap.summary_plot(shap_values   = shap_values, features      = X, feature_names = df.columns[:-1], title         = 'SHAP summary plot')

Summary bar plot

# Generate summary bar plot 
#
shap.summary_plot(shap_values   = shap_values, features      = X, feature_names = df.columns[:-1], plot_type     = "bar")

Dependence plot

# Generate dependence plot
#
shap.dependence_plot(ind               = "worst concave points", shap_values       = shap_values, features          = X, feature_names     = df.columns[:-1],interaction_index = "mean concave points")

Multiple dependence plots

# Generate multiple dependence plots
for name in df.columns[:-1]:shap.dependence_plot(ind               = name, shap_values       = shap_values, features          = X, feature_names     = df.columns[:-1],)