检查树¶

设置¶

In [ ]:

pip install ydf -U

pip install ydf -U

In [4]:

import ydf
import numpy as np

import ydf import numpy as np

检查树的含义是什么？¶

决策森林模型，例如随机森林或梯度提升决策树，是一组决策树的集合。决策树有“内部节点”（即有子节点的节点）和“叶子节点”。使用 get_tree 和 print_tree 方法，你可以检查树的结构、条件和叶子值。

在这个笔记本中，我们将在一个合成数据集上训练一个简单的CART模型，并检查其树结构。

合成数据集¶

我们的数据集由两个输入特征和六个示例组成。

In [5]:

dataset = {
    "x1": np.array([0, 0, 0, 1, 1, 1]),
    "x2": np.array([1, 1, 0, 0, 1, 1]),
    "y": np.array([0, 0, 0, 0, 1, 1]),
}

dataset

dataset = { "x1": np.array([0, 0, 0, 1, 1, 1]), "x2": np.array([1, 1, 0, 0, 1, 1]), "y": np.array([0, 0, 0, 0, 1, 1]), } dataset

Out[5]:

{'x1': array([0, 0, 0, 1, 1, 1]),
 'x2': array([1, 1, 0, 0, 1, 1]),
 'y': array([0, 0, 0, 0, 1, 1])}

训练模型¶

In [8]:

model = ydf.CartLearner(label="y", min_examples=1, task=ydf.Task.REGRESSION).train(dataset)

model.describe()

model = ydf.CartLearner(label="y", min_examples=1, task=ydf.Task.REGRESSION).train(dataset) model.describe()

Train model on 6 examples
Model trained in 0:00:00.000728

Out[8]:

ModelDataspecTrainingVariable importancesStructure

Name : RANDOM_FOREST
Task : REGRESSION
Label : y
Features (2) : x1 x2
Weights : None
Trained with tuner : No
Model size : 3 kB

Number of records: 6
Number of columns: 3

Number of columns by type:
	NUMERICAL: 3 (100%)

Columns:

NUMERICAL: 3 (100%)
	0: "y" NUMERICAL mean:0.333333 min:0 max:1 sd:0.471405
	1: "x1" NUMERICAL mean:0.5 min:0 max:1 sd:0.5
	2: "x2" NUMERICAL mean:0.666667 min:0 max:1 sd:0.471405

Terminology:
	nas: Number of non-available (i.e. missing) values.
	ood: Out of dictionary.
	manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
	tokenized: The attribute value is obtained through tokenization.
	has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
	vocab-size: Number of unique values.

The following evaluation is computed on the validation or out-of-bag dataset.

The Random Forest does not have out-of-bag evaluation training logs. Train the model with compute_oob_performances=True to compute the training logs. Make sure the training logs have not been removed with pure_serving_model=True.

Variable importances measure the importance of an input feature for a model.

INV_MEAN_MIN_DEPTHNUM_AS_ROOTNUM_NODESSUM_SCORE

    1. "x1"  1.000000 ################
    2. "x2"  0.500000 

    1. "x1"  1.000000 

    1. "x1"  1.000000 
    2. "x2"  1.000000 

    1. "x1"  0.666667 
    2. "x2"  0.666667 

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 1

Tree #0:
    "x1">=0.5 [s:0.111111 n:6 np:3 miss:1] ; pred:0.333333
        ├─(pos)─ "x2">=0.5 [s:0.222222 n:3 np:2 miss:1] ; pred:0.666667
        |        ├─(pos)─ pred:1
        |        └─(neg)─ pred:0
        └─(neg)─ pred:0

绘制模型¶

模型的树在model.describe()的“结构”选项卡中可见。您也可以使用print_tree方法打印树。

In [9]:

model.print_tree()

model.print_tree()

'x1' >= 0.5 [score=0.11111 missing=True]
    ├─(pos)─ 'x2' >= 0.5 [score=0.22222 missing=True]
    │        ├─(pos)─ value=1 sd=0
    │        └─(neg)─ value=0 sd=0
    └─(neg)─ value=0 sd=0

访问树结构¶

get_tree 和 get_all_trees 方法以编程方式访问树的结构。

注意： CART 模型只有一棵树，因此 tree_idx 参数设置为 0。对于具有多棵树的模型，可以使用 model.num_trees() 获取树的数量。

In [11]:

tree = model.get_tree(tree_idx=0)

tree

tree = model.get_tree(tree_idx=0) tree

Out[11]:

Tree(root=NonLeaf(value=RegressionValue(num_examples=6.0, value=0.3333333432674408, standard_deviation=0.4714045207910317), condition=NumericalHigherThanCondition(missing=True, score=0.1111111119389534, attribute=1, threshold=0.5), pos_child=NonLeaf(value=RegressionValue(num_examples=3.0, value=0.6666666865348816, standard_deviation=0.4714045207910317), condition=NumericalHigherThanCondition(missing=True, score=0.2222222238779068, attribute=2, threshold=0.5), pos_child=Leaf(value=RegressionValue(num_examples=2.0, value=1.0, standard_deviation=0.0)), neg_child=Leaf(value=RegressionValue(num_examples=1.0, value=0.0, standard_deviation=0.0))), neg_child=Leaf(value=RegressionValue(num_examples=3.0, value=0.0, standard_deviation=0.0))))

你是否认出上面打印的树的结构？你可以访问树的一部分。例如，你可以访问 x2 上的条件：

In [12]:

tree.root.pos_child.condition

tree.root.pos_child.condition

Out[12]:

NumericalHigherThanCondition(missing=True, score=0.2222222238779068, attribute=2, threshold=0.5)

为了以更可读的形式显示树，可以使用 pretty 函数。

In [14]:

print(tree.pretty(model.data_spec()))

print(tree.pretty(model.data_spec()))

'x1' >= 0.5 [score=0.11111 missing=True]
    ├─(pos)─ 'x2' >= 0.5 [score=0.22222 missing=True]
    │        ├─(pos)─ value=1 sd=0
    │        └─(neg)─ value=0 sd=0
    └─(neg)─ value=0 sd=0