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我一直在尝试计算 Python 中 H2O 模块中梯度提升分类器的 SHAP 值。下面是该predict_contibutions方法文档中的改编示例（改编自https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/predict_contributionsShap.ipynb）。

import h2o
import shap
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o import H2OFrame

# initialize H2O
h2o.init()

# load JS visualization code to notebook
shap.initjs()

# Import the prostate dataset
h2o_df = h2o.import_file("https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv")

# Split the data into Train/Test/Validation with Train having 70% and test and validation 15% each
train,test,valid = h2o_df.split_frame(ratios=[.7, .15])

# Convert the response column to a factor
h2o_df["CAPSULE"] = h2o_df["CAPSULE"].asfactor()

# Generate a GBM model using the training dataset
model = H2OGradientBoostingEstimator(distribution="bernoulli",
                                     ntrees=100,
                                     max_depth=4,
                                     learn_rate=0.1)

model.train(y="CAPSULE", x=["AGE","RACE","PSA","GLEASON"],training_frame=h2o_df)

# calculate SHAP values using function predict_contributions
contributions = model.predict_contributions(h2o_df)

# convert the H2O Frame to use with shap's visualization functions
contributions_matrix = contributions.as_data_frame().to_numpy() # the original method is as_matrix()

# shap values are calculated for all features
shap_values = contributions_matrix[:,0:4]

# expected values is the last returned column
expected_value = contributions_matrix[:,4].min()

# force plot for one observation
X=["AGE","RACE","PSA","GLEASON"]
shap.force_plot(expected_value, shap_values[0,:], X)

我从上面的代码中得到的图像是： 一次观察的力图

输出是什么意思？考虑到上面的问题是分类问题，预测值应该是概率（甚至预测的类别——0或1），对吧？基值和预测值均为负值。

谁能帮我这个？

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1 回答 1

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你得到的很可能是对数赔率，而不是概率本身。为了获得概率，您需要将每个对数赔率转换为概率空间，即

p=e^x/(1 + e^x)

当您直接使用SHAP时，您可以通过指定model_output参数来实现：

shap.TreeExplainer(model, data, model_output='probability')
于 2020-06-18T13:04:52.527 回答