利用决策树分分钟生成上千条策略-代码更新

from sklearn.tree import _treefrom sklearn import tree
def XiaoWuGe_Get_Rules(clf,X):    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        # sklearn 模型调整了value改成了概率分布，之前是样本分布，需要更改    if clf.tree_.value.mean()<1:        n_value = np.zeros(shape=clf.tree_.value.shape, dtype=int)        for i in range(0,len(clf.tree_.value)):            n_value[i] = clf.tree_.value[i]*clf.tree_.n_node_samples[i]        value = n_value    else:        value = clf.tree_.value        node_depth = np.zeros(shape=n_nodes, dtype=np.int64)    is_leaves  = np.zeros(shape=n_nodes, dtype=bool)    stack = [(0, 0)]        while len(stack) > 0:        node_id, depth = stack.pop()        node_depth[node_id] = depth            is_split_node = children_left[node_id] != children_right[node_id]                if is_split_node:            stack.append((children_left[node_id],  depth+1))            stack.append((children_right[node_id], depth+1))        else:            is_leaves[node_id] = True      feature_name = [            X.columns[i] if i != _tree.TREE_UNDEFINED else "undefined!"            for i in clf.tree_.feature]        ways  = []    depth = []    feat = []    nodes = []    rules = []    for i in range(n_nodes):           if  is_leaves[i]:             while depth[-1] >= node_depth[i]:                depth.pop()                ways.pop()                    feat.pop()                nodes.pop()            if children_left[i-1]==i:#当前节点是上一个节点的左节点，则是小于                a='{f}<={th}'.format(f=feat[-1],th=round(threshold[nodes[-1]],4))                ways[-1]=a                              last =' & '.join(ways)+':'+str(value[i][0][0])+':'+str(value[i][0][1])                rules.append(last)            else:                a='{f}>{th}'.format(f=feat[-1],th=round(threshold[nodes[-1]],4))                ways[-1]=a                last = ' & '.join(ways)+':'+str(value[i][0][0])+':'+str(value[i][0][1])                rules.append(last)                       else: #不是叶子节点 入栈            if i==0:                ways.append(round(threshold[i],4))                depth.append(node_depth[i])                feat.append(feature_name[i])                nodes.append(i)                         else:                 while depth[-1] >= node_depth[i]:                    depth.pop()                    ways.pop()                    feat.pop()                    nodes.pop()                if i==children_left[nodes[-1]]:                    w='{f}<={th}'.format(f=feat[-1],th=round(threshold[nodes[-1]],4))                else:                    w='{f}>{th}'.format(f=feat[-1],th=round(threshold[nodes[-1]],4))                              ways[-1] = w                  ways.append(round(threshold[i],4))                depth.append(node_depth[i])                 feat.append(feature_name[i])                nodes.append(i)    return rules