生成shap值后使用shap.plots.waterfall时会出现错误

Question

对于下面给出的代码，如果我只使用命令shap.plots.waterfall(shap_values[6])，就会得到错误

'numpy.ndarray‘对象没有属性'base_values’

我必须首先运行这两个命令：

explainer2 = shap.Explainer(clf.best_estimator_.predict, X_train)
shap_values = explainer2(X_train)

然后运行waterfall命令以获得正确的绘图。下面是错误发生的例子：

from sklearn.datasets import make_classification
import seaborn as sns
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import pickle
import joblib
import warnings
import shap
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV

f, (ax1,ax2) = plt.subplots(nrows=1, ncols=2,figsize=(20,8))
# Generate noisy Data
X_train,y_train = make_classification(n_samples=1000, 
                          n_features=50, 
                          n_informative=9, 
                          n_redundant=0, 
                          n_repeated=0, 
                          n_classes=10, 
                          n_clusters_per_class=1,
                          class_sep=9,
                          flip_y=0.2,
                          #weights=[0.5,0.5], 
                          random_state=17)

X_test,y_test = make_classification(n_samples=500, 
                          n_features=50, 
                          n_informative=9, 
                          n_redundant=0, 
                          n_repeated=0, 
                          n_classes=10, 
                          n_clusters_per_class=1,
                          class_sep=9,
                          flip_y=0.2,
                          #weights=[0.5,0.5], 
                          random_state=17)

model = RandomForestClassifier()

parameter_space = {
    'n_estimators': [10,50,100],
    'criterion': ['gini', 'entropy'],
    'max_depth': np.linspace(10,50,11),
}

clf = GridSearchCV(model, parameter_space, cv = 5, scoring = "accuracy", verbose = True) # model
my_model = clf.fit(X_train,y_train)
print(f'Best Parameters: {clf.best_params_}')

# save the model to disk
filename = f'Testt-RF.sav'
pickle.dump(clf, open(filename, 'wb'))

explainer = Explainer(clf.best_estimator_)
shap_values_tr1 = explainer.shap_values(X_train)

shap.plots.waterfall(shap_values[6])

您能告诉我为shap.plots.waterfall数据生成train的正确过程吗？

谢谢!

Answer 1

以下几点对我来说是可行的：

from sklearn.datasets import make_classification
from shap import Explainer, Explanation
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from shap import waterfall_plot

X, y = make_classification(1000, 50, n_informative=9, n_classes=10)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.75, random_state=42)
model = RandomForestClassifier()
model.fit(X_train, y_train)

explainer = Explainer(model)
sv = explainer(X_train)

exp = Explanation(sv[:,:,6], sv.base_values[:,6], X_train, feature_names=None)
idx = 7 # datapoint to explain
waterfall_plot(exp[idx])

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