如何将sklearn决策树规则提取为pandas布尔条件？

有这么多帖子像这样 https://stackoverflow.com/questions/20224526/how-to-extract-the-decision-rules-from-scikit-learn-decision-tree关于如何提取 sklearn 决策树规则，但我找不到任何有关使用 pandas 的信息。

Take 这个数据和模型 https://www.datacamp.com/community/tutorials/decision-tree-classification-python例如，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，请注意数据框是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 决策树规则的高手。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 pandas 布尔条件。

首先我们使用 scikit文档 https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html在决策树结构上获取有关所构建的树的信息：

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来创建特定节点（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具体规则：

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)

根据您提供的数据，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，因此您不能直接使用它们来调用它们df[rules[3]]，你必须像这样使用 eval 函数df[eval(rules[3])]