对于决策树的Python实现,我还比较陌生。我试图提取规则以仅预测子节点,并且我需要它能够预测新数据的概率分数(而不仅仅是最终分类),并可能将算法转移给其他用户。有一个简单的方法吗?我在({{3)}找到了一些解决方案。但是,当我测试它们时,由于某种原因(我的树很大且很深),我无法获得所有的子节点。任何建议表示赞赏。谢谢。
我已经更新了上面链接中的第一个代码以生成节点,它似乎与大树配合使用效果最佳。但是,我很难使它与pd Dataframes一起使用。这是示例: 将熊猫作为pd导入 将numpy导入为np 从sklearn.tree导入DecisionTreeClassifier
df = pd.DataFrame({'col1':[0,1,2,3],'col2':[3,4,5,6],'dv':[0,1,0,1]})
df
# create decision tree
dt = DecisionTreeClassifier(random_state=0, max_depth=5, min_samples_leaf=1)
dt.fit(df.loc[:,('col1','col2')], df.dv)
from sklearn.tree import _tree
def tree_to_code(tree, feature_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print ("def tree({}):".format(", ".join(feature_names)))
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print ("{}if {} <= {}:".format(indent, name, threshold))
recurse(tree_.children_left[node], depth + 1)
print ("{}else: # if {} > {}".format(indent, name, threshold))
recurse(tree_.children_right[node], depth + 1)
else:
print ("{}return {}".format(indent, node))
recurse(0, 1)
tree_to_code(dt, df.columns)
上面的调用产生以下代码:
def tree(col1, col2, dv):
if col2 <= 3.5:
return 1
else: # if col2 > 3.5
if col1 <= 1.5:
return 3
else: # if col1 > 1.5
if col1 <= 2.5:
return 5
else: # if col1 > 2.5
return 6
而且,当我如下调用上述代码时,出现错误,提示我缺少一个参数。如何修改代码以使其在pandas DataFrame上运行?
tree('col1', 'col2', 'dv_pred')
答案 0 :(得分:1)
这是一个可行的解决方案
import pandas as pd
from sklearn.tree import _tree
from sklearn.tree import DecisionTreeClassifier
df = pd.DataFrame({'col1':[0,1,2,3],'col2':[3,4,5,6],'dv':[0,1,0,1]})
# create decision tree
dt = DecisionTreeClassifier(random_state=0, max_depth=5, min_samples_leaf=1)
features = ['col1','col2']
dt.fit(df.loc[:,features], df.dv)
def tree_to_code(tree, feature_names):
tree_ = tree.tree_
feature_name = [
feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!"
for i in tree_.feature
]
print ("def tree(x):")
def recurse(node, depth):
indent = " " * depth
if tree_.feature[node] != _tree.TREE_UNDEFINED:
name = feature_name[node]
threshold = tree_.threshold[node]
print ("{}if x['{}'] <= {}:".format(indent, name, threshold))
recurse(tree_.children_left[node], depth + 1)
print ("{}else: # if x['{}'] > {}".format(indent, name, threshold))
recurse(tree_.children_right[node], depth + 1)
else:
print ("{}return {}".format(indent, node))
recurse(0, 1)
tree_to_code(dt, df[features].columns)
然后获取预测
df.apply(tree, axis=1)