python - 如何将sklearn决策树规则提取为pandas布尔条件?
问题描述
有很多关于如何提取 sklearn 决策树规则的帖子,但我找不到任何关于使用 pandas 的帖子。
以这个数据和模型为例,如下
# Create Decision Tree classifer object
clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)
结果:
预期的:
这个例子有 8 条规则。
从左到右,注意数据框是df
r1 = (df['glucose']<=127.5) & (df['bmi']<=26.45) & (df['bmi']<=9.1)
……
r8 = (df['glucose']>127.5) & (df['bmi']>28.15) & (df['glucose']>158.5)
我不是提取 sklearn 决策树规则的大师。获取 pandas 布尔条件将帮助我计算每个规则的样本和其他指标。所以我想将每个规则提取为熊猫布尔条件。
解决方案
首先,让我们使用关于决策树结构的 scikit文档来获取有关所构建树的信息:
n_nodes = clf.tree_.node_count
children_left = clf.tree_.children_left
children_right = clf.tree_.children_right
feature = clf.tree_.feature
threshold = clf.tree_.threshold
然后我们定义两个递归函数。第一个将找到从树根开始的路径以创建特定节点(在我们的例子中是所有叶子)。第二个将编写用于使用其创建路径创建节点的特定规则:
def find_path(node_numb, path, x):
path.append(node_numb)
if node_numb == x:
return True
left = False
right = False
if (children_left[node_numb] !=-1):
left = find_path(children_left[node_numb], path, x)
if (children_right[node_numb] !=-1):
right = find_path(children_right[node_numb], path, x)
if left or right :
return True
path.remove(node_numb)
return False
def get_rule(path, column_names):
mask = ''
for index, node in enumerate(path):
#We check if we are not in the leaf
if index!=len(path)-1:
# Do we go under or over the threshold ?
if (children_left[node] == path[index+1]):
mask += "(df['{}']<= {}) \t ".format(column_names[feature[node]], threshold[node])
else:
mask += "(df['{}']> {}) \t ".format(column_names[feature[node]], threshold[node])
# We insert the & at the right places
mask = mask.replace("\t", "&", mask.count("\t") - 1)
mask = mask.replace("\t", "")
return mask
最后,我们使用这两个函数首先存储每个叶子的创建路径。然后存储用于创建每个叶子的规则:
# Leaves
leave_id = clf.apply(X_test)
paths ={}
for leaf in np.unique(leave_id):
path_leaf = []
find_path(0, path_leaf, leaf)
paths[leaf] = np.unique(np.sort(path_leaf))
rules = {}
for key in paths:
rules[key] = get_rule(paths[key], pima.columns)
使用您提供的数据,输出为:
rules =
{3: "(df['insulin']<= 127.5) & (df['bp']<= 26.450000762939453) & (df['bp']<= 9.100000381469727) ",
4: "(df['insulin']<= 127.5) & (df['bp']<= 26.450000762939453) & (df['bp']> 9.100000381469727) ",
6: "(df['insulin']<= 127.5) & (df['bp']> 26.450000762939453) & (df['skin']<= 27.5) ",
7: "(df['insulin']<= 127.5) & (df['bp']> 26.450000762939453) & (df['skin']> 27.5) ",
10: "(df['insulin']> 127.5) & (df['bp']<= 28.149999618530273) & (df['insulin']<= 145.5) ",
11: "(df['insulin']> 127.5) & (df['bp']<= 28.149999618530273) & (df['insulin']> 145.5) ",
13: "(df['insulin']> 127.5) & (df['bp']> 28.149999618530273) & (df['insulin']<= 158.5) ",
14: "(df['insulin']> 127.5) & (df['bp']> 28.149999618530273) & (df['insulin']> 158.5) "}
由于规则是字符串,你不能直接调用它们 using df[rules[3]]
,你必须像这样使用 eval 函数df[eval(rules[3])]
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