将自定义树模型加载到 SHAP 的示例
本笔记本展示了如何将自定义的树集成模型传递给SHAP进行解释。
[1]:
import graphviz
import numpy as np
import scipy
import sklearn
import shap
简单的回归树模型
在这里，我们定义了一个简单的回归树，然后将其作为自定义模型加载到SHAP中。
[2]:
X, y = shap.datasets.adult()
orig_model = sklearn.tree.DecisionTreeRegressor(max_depth=2)
orig_model.fit(X, y)
[2]:
DecisionTreeRegressor(max_depth=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeRegressor(max_depth=2)
[3]:
dot_data = sklearn.tree.export_graphviz(
orig_model, out_file=None, filled=True, rounded=True, special_characters=True
)
graph = graphviz.Source(dot_data)
graph
[3]:
有关这些属性具体含义的更多信息，请参阅 scikit-learn 文档
[4]:
# extract the arrays that define the tree
children_left = orig_model.tree_.children_left
children_right = orig_model.tree_.children_right
children_default = children_right.copy() # because sklearn does not use missing values
features = orig_model.tree_.feature
thresholds = orig_model.tree_.threshold
values = orig_model.tree_.value.reshape(orig_model.tree_.value.shape[0], 1)
node_sample_weight = orig_model.tree_.weighted_n_node_samples
print(
" children_left", children_left
) # note that negative children values mean this is a leaf node
print(" children_right", children_right)
print(" children_default", children_default)
print(" features", features)
print(" thresholds", thresholds.round(3)) # -2 means the node is a leaf node
print(" values", values.round(3))
print("node_sample_weight", node_sample_weight)
children_left [ 1 2 -1 -1 5 -1 -1]
children_right [ 4 3 -1 -1 6 -1 -1]
children_default [ 4 3 -1 -1 6 -1 -1]
features [ 5 8 -2 -2 2 -2 -2]
thresholds [ 3.5000e+00 7.0735e+03 -2.0000e+00 -2.0000e+00 1.2500e+01 -2.0000e+00
-2.0000e+00]
values [[0.241]
[0.066]
[0.05 ]
[0.962]
[0.451]
[0.335]
[0.724]]
node_sample_weight [32561. 17800. 17482. 318. 14761. 10329. 4432.]
[5]:
# define a custom tree model
tree_dict = {
"children_left": children_left,
"children_right": children_right,
"children_default": children_default,
"features": features,
"thresholds": thresholds,
"values": values,
"node_sample_weight": node_sample_weight,
}
model = {"trees": [tree_dict]}
[6]:
explainer = shap.TreeExplainer(model)
[7]:
# Make sure that the ingested SHAP model (a TreeEnsemble object) makes the
# same predictions as the original model
assert np.abs(explainer.model.predict(X) - orig_model.predict(X)).max() < 1e-4
[8]:
# make sure the SHAP values sum up to the model output (this is the local accuracy property)
assert (
np.abs(
explainer.expected_value
+ explainer.shap_values(X).sum(1)
- orig_model.predict(X)
).max()
< 1e-4
)
简单的GBM分类模型（包含2棵树）
在这里，我们定义了一个简单的梯度提升分类器，然后将其作为自定义模型加载到SHAP中。
[9]:
X2, y2 = shap.datasets.adult()
orig_model2 = sklearn.ensemble.GradientBoostingClassifier(n_estimators=2)
orig_model2.fit(X2, y2)
[9]:
GradientBoostingClassifier(n_estimators=2)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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GradientBoostingClassifier(n_estimators=2)
拉取第一棵树的信息
[10]:
tree_tmp = orig_model2.estimators_[0][0].tree_
# extract the arrays that define the tree
children_left1 = tree_tmp.children_left
children_right1 = tree_tmp.children_right
children_default1 = (
children_right1.copy()
) # because sklearn does not use missing values
features1 = tree_tmp.feature
thresholds1 = tree_tmp.threshold
values1 = tree_tmp.value.reshape(tree_tmp.value.shape[0], 1)
node_sample_weight1 = tree_tmp.weighted_n_node_samples
print(
" children_left1", children_left1
) # note that negative children values mean this is a leaf node
print(" children_right1", children_right1)
print(" children_default1", children_default1)
print(" features1", features1)
print(" thresholds1", thresholds1.round(3))
print(" values1", values1.round(3))
print("node_sample_weight1", node_sample_weight1)
children_left1 [ 1 2 3 -1 -1 6 -1 -1 9 10 -1 -1 13 -1 -1]
children_right1 [ 8 5 4 -1 -1 7 -1 -1 12 11 -1 -1 14 -1 -1]
children_default1 [ 8 5 4 -1 -1 7 -1 -1 12 11 -1 -1 14 -1 -1]
features1 [ 5 8 2 -2 -2 0 -2 -2 2 8 -2 -2 8 -2 -2]
thresholds1 [ 3.5000e+00 7.0735e+03 1.2500e+01 -2.0000e+00 -2.0000e+00 2.0500e+01
-2.0000e+00 -2.0000e+00 1.2500e+01 5.0955e+03 -2.0000e+00 -2.0000e+00
5.0955e+03 -2.0000e+00 -2.0000e+00]
values1 [[-0. ]
[-0.175]
[-0.191]
[-1.177]
[-0.503]
[ 0.721]
[-0.223]
[ 4.013]
[ 0.211]
[ 0.094]
[ 0.325]
[ 4.048]
[ 0.483]
[ 2.372]
[ 4.128]]
node_sample_weight1 [3.2561e+04 1.7800e+04 1.7482e+04 1.4036e+04 3.4460e+03 3.1800e+02
5.0000e+00 3.1300e+02 1.4761e+04 1.0329e+04 9.8070e+03 5.2200e+02
4.4320e+03 3.7540e+03 6.7800e+02]
拉取第二个树的信息
[11]:
tree_tmp = orig_model2.estimators_[1][0].tree_
# extract the arrays that define the tree
children_left2 = tree_tmp.children_left
children_right2 = tree_tmp.children_right
children_default2 = (
children_right2.copy()
) # because sklearn does not use missing values
features2 = tree_tmp.feature
thresholds2 = tree_tmp.threshold
values2 = tree_tmp.value.reshape(tree_tmp.value.shape[0], 1)
node_sample_weight2 = tree_tmp.weighted_n_node_samples
print(
" children_left2", children_left2
) # note that negative children values mean this is a leaf node
print(" children_right2", children_right2)
print(" children_default2", children_default2)
print(" features2", features2)
print(" thresholds2", thresholds2.round(3))
print(" values2", values2.round(3))
print("node_sample_weight2", node_sample_weight2)
children_left2 [ 1 2 3 -1 -1 6 -1 -1 9 10 -1 -1 13 -1 -1]
children_right2 [ 8 5 4 -1 -1 7 -1 -1 12 11 -1 -1 14 -1 -1]
children_default2 [ 8 5 4 -1 -1 7 -1 -1 12 11 -1 -1 14 -1 -1]
features2 [ 5 8 2 -2 -2 0 -2 -2 2 8 -2 -2 8 -2 -2]
thresholds2 [ 3.5000e+00 7.0735e+03 1.3500e+01 -2.0000e+00 -2.0000e+00 2.0500e+01
-2.0000e+00 -2.0000e+00 1.2500e+01 5.0955e+03 -2.0000e+00 -2.0000e+00
5.0955e+03 -2.0000e+00 -2.0000e+00]
values2 [[-1.000e-03]
[-1.580e-01]
[-1.720e-01]
[-1.062e+00]
[ 1.360e-01]
[ 6.420e-01]
[-2.030e-01]
[ 2.993e+00]
[ 1.880e-01]
[ 8.400e-02]
[ 2.870e-01]
[ 3.015e+00]
[ 4.310e-01]
[ 1.895e+00]
[ 3.066e+00]]
node_sample_weight2 [3.2561e+04 1.7800e+04 1.7482e+04 1.6560e+04 9.2200e+02 3.1800e+02
5.0000e+00 3.1300e+02 1.4761e+04 1.0329e+04 9.8070e+03 5.2200e+02
4.4320e+03 3.7540e+03 6.7800e+02]
创建一个 SHAP 树列表
[12]:
# define a custom tree model
tree_dicts = [
{
"children_left": children_left1,
"children_right": children_right1,
"children_default": children_default1,
"features": features1,
"thresholds": thresholds1,
"values": values1 * orig_model2.learning_rate,
"node_sample_weight": node_sample_weight1,
},
{
"children_left": children_left2,
"children_right": children_right2,
"children_default": children_default2,
"features": features2,
"thresholds": thresholds2,
"values": values2 * orig_model2.learning_rate,
"node_sample_weight": node_sample_weight2,
},
]
model2 = {
"trees": tree_dicts,
"base_offset": scipy.special.logit(orig_model2.init_.class_prior_[1]),
"tree_output": "log_odds",
"objective": "binary_crossentropy",
"input_dtype": np.float32, # this is what type the model uses the input feature data
"internal_dtype": np.float64, # this is what type the model uses for values and thresholds
}
解释自定义模型
[13]:
# build a background dataset for us to use based on people near a 0.95 cutoff
vs = np.abs(orig_model2.predict_proba(X2)[:, 1] - 0.95)
inds = np.argsort(vs)
inds = inds[:200]
[14]:
# build an explainer that explains the probability output of the model
explainer2 = shap.TreeExplainer(
model2,
X2.iloc[inds, :],
feature_perturbation="interventional",
model_output="probability",
)
[15]:
# Make sure that the ingested SHAP model (a TreeEnsemble object) makes the
# same predictions as the original model
assert (
np.abs(
explainer2.model.predict(X2, output="probability")
- orig_model2.predict_proba(X2)[:, 1]
).max()
< 1e-4
)
[16]:
# make sure the sum of the SHAP values equals the model output
shap_sum = explainer2.expected_value + explainer2.shap_values(X2.iloc[:, :]).sum(1)
assert np.abs(shap_sum - orig_model2.predict_proba(X2)[:, 1]).max() < 1e-4