擅长:python、mysql、java
<p>Shap以<code>LGBM</code>的方式对<code>pred_contrib=True</code>进行赋值:</p>
<pre><code>from lightgbm.sklearn import LGBMClassifier
from sklearn.datasets import load_iris
X,y = load_iris(return_X_y=True)
lgbm = LGBMClassifier()
lgbm.fit(X,y)
lgbm_shap = lgbm.predict(X, pred_contrib=True)
# Shape of returned LGBM shap values: 4 features x 3 classes + 3 expected values over the training dataset
print(lgbm_shap.shape)
# 0th row of LGBM shap values for 0th feature
print(lgbm_shap[0,:4])
</code></pre>
<p>输出:</p>
<pre><code>(150, 15)
[-0.0176954 0.50644615 5.56584344 3.43032313]
</code></pre>
<p>来自<code>shap</code>的形状值:</p>
<pre><code>import shap
explainer = shap.TreeExplainer(lgbm)
shap_values = explainer.shap_values(X)
# num of predicted classes
print(len(shap_values))
# shap values for 0th class for 0th row
print(shap_values[0][0])
</code></pre>
<p>输出:</p>
<pre><code>3
array([-0.0176954 , 0.50644615, 5.56584344, 3.43032313])
</code></pre>
<p>在我看来是一样的</p>