Tool Series: TensorFlow Decision Forest_(7) Check and debug the decision forest model

Article directory

In this article, you will learn how to directly examine and create the structure of your model. We assume that you are already familiar with the concepts introduced at the beginner and intermediate levels.

In this article you will:

Train a random forest model and access its structure programmatically.
Create a random forest model manually and use it as a classic model.

set up

# 安装 TensorFlow Decision Forests 库
!pip install tensorflow_decision_forests

# 安装 wurlitzer 库，用于显示训练日志
!pip install wurlitzer

Collecting tensorflow_decision_forests
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# 导入tensorflow_decision_forests库
import tensorflow_decision_forests as tfdf

# 导入os、numpy、pandas、tensorflow、matplotlib.pyplot、math、collections库
import os
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import math
import collections

2022-12-14 12:24:51.050867: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:24:51.050964: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-14 12:24:51.050973: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.

Hidden code cells limit the output height in colab.


# 导入所需的模块
from IPython.core.magic import register_line_magic
from IPython.display import Javascript
from IPython.display import display as ipy_display

# 定义一个魔术命令，用于设置单元格的最大高度
@register_line_magic
def set_cell_height(size):
  # 调用Javascript代码，设置单元格的最大高度
  ipy_display(
      Javascript("google.colab.output.setIframeHeight(0, true, {maxHeight: " +
                 str(size) + "})"))

Training a simple random forest

We train a random forest like in beginner colab .

# 下载数据集
!wget -q https://storage.googleapis.com/download.tensorflow.org/data/palmer_penguins/penguins.csv -O /tmp/penguins.csv

# 将数据集加载到Pandas Dataframe中
dataset_df = pd.read_csv("/tmp/penguins.csv")

# 显示前三个示例
print(dataset_df.head(3))

# 将Pandas Dataframe转换为tf数据集
dataset_tf = tfdf.keras.pd_dataframe_to_tf_dataset(dataset_df, label="species")

# 训练随机森林模型
model = tfdf.keras.RandomForestModel(compute_oob_variable_importances=True)
model.fit(x=dataset_tf)

  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \
0  Adelie  Torgersen            39.1           18.7              181.0   
1  Adelie  Torgersen            39.5           17.4              186.0   
2  Adelie  Torgersen            40.3           18.0              195.0   

   body_mass_g     sex  year  
0       3750.0    male  2007  
1       3800.0  female  2007  
2       3250.0  female  2007  
Warning: The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.


WARNING:absl:The `num_threads` constructor argument is not set and the number of CPU is os.cpu_count()=32 > 32. Setting num_threads to 32. Set num_threads manually to use more than 32 cpus.


Use /tmpfs/tmp/tmpvr7urazn as temporary training directory
Reading training dataset...
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089


WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.
Instructions for updating:
Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089


Training dataset read in 0:00:02.961832. Found 344 examples.
Training model...
Model trained in 0:00:00.093680
Compiling model...


[INFO 2022-12-14T12:24:58.955519768+00:00 kernel.cc:1175] Loading model from path /tmpfs/tmp/tmpvr7urazn/model/ with prefix fb8057db01324481
[INFO 2022-12-14T12:24:58.971817533+00:00 abstract_model.cc:1306] Engine "RandomForestGeneric" built
[INFO 2022-12-14T12:24:58.97187255+00:00 kernel.cc:1021] Use fast generic engine


WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f9b54f644c0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert


WARNING:tensorflow:AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f9b54f644c0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert


WARNING: AutoGraph could not transform <function simple_ml_inference_op_with_handle at 0x7f9b54f644c0> and will run it as-is.
Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.
Cause: could not get source code
To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert
Model compiled.





<keras.callbacks.History at 0x7f9b5394c6d0>

compute_oob_variable_importances=TruePlease note the hyperparameters in the model constructor . This option calculates out-of-bag (OOB) variable importance during training. This is a popular permutation variable importance for random forest models .

Calculating OOB variable importance will not affect the final model, but will slow down training on large datasets.

Please check the model summary:

# 打印模型的概述信息
model.summary()

<IPython.core.display.Javascript object>


Model: "random_forest_model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
=================================================================
Total params: 1
Trainable params: 0
Non-trainable params: 1
_________________________________________________________________
Type: "RANDOM_FOREST"
Task: CLASSIFICATION
Label: "__LABEL"

Input Features (7):
	bill_depth_mm
	bill_length_mm
	body_mass_g
	flipper_length_mm
	island
	sex
	year

No weights

Variable Importance: MEAN_DECREASE_IN_ACCURACY:
    1.    "bill_length_mm"  0.151163 ################
    2.            "island"  0.008721 #
    3.     "bill_depth_mm"  0.000000 
    4.       "body_mass_g"  0.000000 
    5.               "sex"  0.000000 
    6.              "year"  0.000000 
    7. "flipper_length_mm" -0.002907 

Variable Importance: MEAN_DECREASE_IN_AP_1_VS_OTHERS:
    1.    "bill_length_mm"  0.083305 ################
    2.            "island"  0.007664 #
    3. "flipper_length_mm"  0.003400 
    4.     "bill_depth_mm"  0.002741 
    5.       "body_mass_g"  0.000722 
    6.               "sex"  0.000644 
    7.              "year"  0.000000 

Variable Importance: MEAN_DECREASE_IN_AP_2_VS_OTHERS:
    1.    "bill_length_mm"  0.508510 ################
    2.            "island"  0.023487 
    3.     "bill_depth_mm"  0.007744 
    4. "flipper_length_mm"  0.006008 
    5.       "body_mass_g"  0.003017 
    6.               "sex"  0.001537 
    7.              "year" -0.000245 

Variable Importance: MEAN_DECREASE_IN_AP_3_VS_OTHERS:
    1.            "island"  0.002192 ################
    2.    "bill_length_mm"  0.001572 ############
    3.     "bill_depth_mm"  0.000497 #######
    4.               "sex"  0.000000 ####
    5.              "year"  0.000000 ####
    6.       "body_mass_g" -0.000053 ####
    7. "flipper_length_mm" -0.000890 

Variable Importance: MEAN_DECREASE_IN_AUC_1_VS_OTHERS:
    1.    "bill_length_mm"  0.071306 ################
    2.            "island"  0.007299 #
    3. "flipper_length_mm"  0.004506 #
    4.     "bill_depth_mm"  0.002124 
    5.       "body_mass_g"  0.000548 
    6.               "sex"  0.000480 
    7.              "year"  0.000000 

Variable Importance: MEAN_DECREASE_IN_AUC_2_VS_OTHERS:
    1.    "bill_length_mm"  0.108642 ################
    2.            "island"  0.014493 ##
    3.     "bill_depth_mm"  0.007406 #
    4. "flipper_length_mm"  0.005195 
    5.       "body_mass_g"  0.001012 
    6.               "sex"  0.000480 
    7.              "year" -0.000053 

Variable Importance: MEAN_DECREASE_IN_AUC_3_VS_OTHERS:
    1.            "island"  0.002126 ################
    2.    "bill_length_mm"  0.001393 ###########
    3.     "bill_depth_mm"  0.000293 #####
    4.               "sex"  0.000000 ###
    5.              "year"  0.000000 ###
    6.       "body_mass_g" -0.000037 ###
    7. "flipper_length_mm" -0.000550 

Variable Importance: MEAN_DECREASE_IN_PRAUC_1_VS_OTHERS:
    1.    "bill_length_mm"  0.083122 ################
    2.            "island"  0.010887 ##
    3. "flipper_length_mm"  0.003425 
    4.     "bill_depth_mm"  0.002731 
    5.       "body_mass_g"  0.000719 
    6.               "sex"  0.000641 
    7.              "year"  0.000000 

Variable Importance: MEAN_DECREASE_IN_PRAUC_2_VS_OTHERS:
    1.    "bill_length_mm"  0.497611 ################
    2.            "island"  0.024045 
    3.     "bill_depth_mm"  0.007734 
    4. "flipper_length_mm"  0.006017 
    5.       "body_mass_g"  0.003000 
    6.               "sex"  0.001528 
    7.              "year" -0.000243 

Variable Importance: MEAN_DECREASE_IN_PRAUC_3_VS_OTHERS:
    1.            "island"  0.002187 ################
    2.    "bill_length_mm"  0.001568 ############
    3.     "bill_depth_mm"  0.000495 #######
    4.               "sex"  0.000000 ####
    5.              "year"  0.000000 ####
    6.       "body_mass_g" -0.000053 ####
    7. "flipper_length_mm" -0.000886 

Variable Importance: MEAN_MIN_DEPTH:
    1.           "__LABEL"  3.479602 ################
    2.              "year"  3.463891 ###############
    3.               "sex"  3.430498 ###############
    4.       "body_mass_g"  2.898112 ###########
    5.            "island"  2.388925 ########
    6.     "bill_depth_mm"  2.336100 #######
    7.    "bill_length_mm"  1.282960 
    8. "flipper_length_mm"  1.270079 

Variable Importance: NUM_AS_ROOT:
    1. "flipper_length_mm" 157.000000 ################
    2.    "bill_length_mm" 76.000000 #######
    3.     "bill_depth_mm" 52.000000 #####
    4.            "island" 12.000000 
    5.       "body_mass_g"  3.000000 

Variable Importance: NUM_NODES:
    1.    "bill_length_mm" 778.000000 ################
    2.     "bill_depth_mm" 463.000000 #########
    3. "flipper_length_mm" 414.000000 ########
    4.            "island" 342.000000 ######
    5.       "body_mass_g" 338.000000 ######
    6.               "sex" 36.000000 
    7.              "year" 19.000000 

Variable Importance: SUM_SCORE:
    1.    "bill_length_mm" 36515.793787 ################
    2. "flipper_length_mm" 35120.434174 ###############
    3.            "island" 14669.408395 ######
    4.     "bill_depth_mm" 14515.446617 ######
    5.       "body_mass_g" 3485.330881 #
    6.               "sex" 354.201073 
    7.              "year" 49.737758 



Winner takes all: true
Out-of-bag evaluation: accuracy:0.976744 logloss:0.0678223
Number of trees: 300
Total number of nodes: 5080

Number of nodes by tree:
Count: 300 Average: 16.9333 StdDev: 3.10197
Min: 11 Max: 31 Ignored: 0
----------------------------------------------
[ 11, 12)  6   2.00%   2.00% #
[ 12, 13)  0   0.00%   2.00%
[ 13, 14) 46  15.33%  17.33% #####
[ 14, 15)  0   0.00%  17.33%
[ 15, 16) 70  23.33%  40.67% ########
[ 16, 17)  0   0.00%  40.67%
[ 17, 18) 84  28.00%  68.67% ##########
[ 18, 19)  0   0.00%  68.67%
[ 19, 20) 46  15.33%  84.00% #####
[ 20, 21)  0   0.00%  84.00%
[ 21, 22) 30  10.00%  94.00% ####
[ 22, 23)  0   0.00%  94.00%
[ 23, 24) 13   4.33%  98.33% ##
[ 24, 25)  0   0.00%  98.33%
[ 25, 26)  2   0.67%  99.00%
[ 26, 27)  0   0.00%  99.00%
[ 27, 28)  2   0.67%  99.67%
[ 28, 29)  0   0.00%  99.67%
[ 29, 30)  0   0.00%  99.67%
[ 30, 31]  1   0.33% 100.00%

Depth by leafs:
Count: 2690 Average: 3.53271 StdDev: 1.06789
Min: 2 Max: 7 Ignored: 0
----------------------------------------------
[ 2, 3) 545  20.26%  20.26% ######
[ 3, 4) 747  27.77%  48.03% ########
[ 4, 5) 888  33.01%  81.04% ##########
[ 5, 6) 444  16.51%  97.55% #####
[ 6, 7)  62   2.30%  99.85% #
[ 7, 7]   4   0.15% 100.00%

Number of training obs by leaf:
Count: 2690 Average: 38.3643 StdDev: 44.8651
Min: 5 Max: 155 Ignored: 0
----------------------------------------------
[   5,  12) 1474  54.80%  54.80% ##########
[  12,  20)  124   4.61%  59.41% #
[  20,  27)   48   1.78%  61.19%
[  27,  35)   74   2.75%  63.94% #
[  35,  42)   58   2.16%  66.10%
[  42,  50)   85   3.16%  69.26% #
[  50,  57)   96   3.57%  72.83% #
[  57,  65)   87   3.23%  76.06% #
[  65,  72)   49   1.82%  77.88%
[  72,  80)   23   0.86%  78.74%
[  80,  88)   30   1.12%  79.85%
[  88,  95)   23   0.86%  80.71%
[  95, 103)   42   1.56%  82.27%
[ 103, 110)   62   2.30%  84.57%
[ 110, 118)  115   4.28%  88.85% #
[ 118, 125)  115   4.28%  93.12% #
[ 125, 133)   98   3.64%  96.77% #
[ 133, 140)   49   1.82%  98.59%
[ 140, 148)   31   1.15%  99.74%
[ 148, 155]    7   0.26% 100.00%

Attribute in nodes:
	778 : bill_length_mm [NUMERICAL]
	463 : bill_depth_mm [NUMERICAL]
	414 : flipper_length_mm [NUMERICAL]
	342 : island [CATEGORICAL]
	338 : body_mass_g [NUMERICAL]
	36 : sex [CATEGORICAL]
	19 : year [NUMERICAL]

Attribute in nodes with depth <= 0:
	157 : flipper_length_mm [NUMERICAL]
	76 : bill_length_mm [NUMERICAL]
	52 : bill_depth_mm [NUMERICAL]
	12 : island [CATEGORICAL]
	3 : body_mass_g [NUMERICAL]

Attribute in nodes with depth <= 1:
	250 : bill_length_mm [NUMERICAL]
	244 : flipper_length_mm [NUMERICAL]
	183 : bill_depth_mm [NUMERICAL]
	170 : island [CATEGORICAL]
	53 : body_mass_g [NUMERICAL]

Attribute in nodes with depth <= 2:
	462 : bill_length_mm [NUMERICAL]
	320 : flipper_length_mm [NUMERICAL]
	310 : bill_depth_mm [NUMERICAL]
	287 : island [CATEGORICAL]
	162 : body_mass_g [NUMERICAL]
	9 : sex [CATEGORICAL]
	5 : year [NUMERICAL]

Attribute in nodes with depth <= 3:
	669 : bill_length_mm [NUMERICAL]
	410 : bill_depth_mm [NUMERICAL]
	383 : flipper_length_mm [NUMERICAL]
	328 : island [CATEGORICAL]
	286 : body_mass_g [NUMERICAL]
	32 : sex [CATEGORICAL]
	10 : year [NUMERICAL]

Attribute in nodes with depth <= 5:
	778 : bill_length_mm [NUMERICAL]
	462 : bill_depth_mm [NUMERICAL]
	413 : flipper_length_mm [NUMERICAL]
	342 : island [CATEGORICAL]
	338 : body_mass_g [NUMERICAL]
	36 : sex [CATEGORICAL]
	19 : year [NUMERICAL]

Condition type in nodes:
	2012 : HigherCondition
	378 : ContainsBitmapCondition
Condition type in nodes with depth <= 0:
	288 : HigherCondition
	12 : ContainsBitmapCondition
Condition type in nodes with depth <= 1:
	730 : HigherCondition
	170 : ContainsBitmapCondition
Condition type in nodes with depth <= 2:
	1259 : HigherCondition
	296 : ContainsBitmapCondition
Condition type in nodes with depth <= 3:
	1758 : HigherCondition
	360 : ContainsBitmapCondition
Condition type in nodes with depth <= 5:
	2010 : HigherCondition
	378 : ContainsBitmapCondition
Node format: NOT_SET

Training OOB:
	trees: 1, Out-of-bag evaluation: accuracy:0.964286 logloss:1.28727
	trees: 13, Out-of-bag evaluation: accuracy:0.959064 logloss:0.4869
	trees: 31, Out-of-bag evaluation: accuracy:0.95614 logloss:0.284603
	trees: 54, Out-of-bag evaluation: accuracy:0.973837 logloss:0.175283
	trees: 73, Out-of-bag evaluation: accuracy:0.97093 logloss:0.175816
	trees: 85, Out-of-bag evaluation: accuracy:0.973837 logloss:0.171781
	trees: 96, Out-of-bag evaluation: accuracy:0.97093 logloss:0.077417
	trees: 116, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0761788
	trees: 127, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0745239
	trees: 137, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0753508
	trees: 150, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0741464
	trees: 160, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0749481
	trees: 170, Out-of-bag evaluation: accuracy:0.979651 logloss:0.0719624
	trees: 190, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0711787
	trees: 203, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0701121
	trees: 213, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0682979
	trees: 224, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0689686
	trees: 248, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0674086
	trees: 260, Out-of-bag evaluation: accuracy:0.976744 logloss:0.068218
	trees: 270, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0680733
	trees: 280, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0685965
	trees: 290, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0683421
	trees: 300, Out-of-bag evaluation: accuracy:0.976744 logloss:0.0678223

Note that variable importance has multiple names MEAN_DECREASE_IN_*.

Draw model

Next, draw the model.

Random Forest is a huge model (the model has 300 trees and ~5k nodes; see summary above). Therefore, only the first tree is drawn and the nodes are limited to depth 3.

# 使用model_plotter模块中的plot_model_in_colab函数来绘制模型
# 参数model表示要绘制的模型
# 参数tree_idx表示要绘制的树的索引，这里设置为0表示绘制第一棵树
# 参数max_depth表示要绘制的树的最大深度，这里设置为3表示绘制到第三层
tfdf.model_plotter.plot_model_in_colab(model, tree_idx=0, max_depth=3)

/**

Plotting of decision trees generated by TF-DF.
A tree is a recursive structure of node objects.
A node contains one or more of the following components:
- A value: Representing the output of the node. If the node is not a leaf,

 the value is only present for analysis i.e. it is not used for

```
 predictions.
```
- A condition : For non-leaf nodes, the condition (also known as split)

 defines a binary test to branch to the positive or negative child.

- An explanation: Generally a plot showing the relation between the label

 and the condition to give insights about the effect of the condition.

- Two children : For non-leaf nodes, the children nodes. The first

 children (i.e. "node.children[0]") is the negative children (drawn in

 red). The second children is the positive one (drawn in green).

/**

Plots a single decision tree into a DOM element.
@param {!options} options Dictionary of configurations.
@param {!tree} raw_tree Recursive tree structure.
@param {string} canvas_id Id of the output dom element.
*/
function display_tree(options, raw_tree, canvas_id) {
console.log(options);

// Determine the node placement.
const tree_struct = d3.tree().nodeSize(
[options.node_y_offset, options.node_x_offset])(d3.hierarchy(raw_tree));

// Boundaries of the node placement.
let x_min = Infinity;
let x_max = -x_min;
let y_min = Infinity;
let y_max = -x_min;

tree_struct.each(d => {
if (d.x > x_max) x_max = d.x;
if (d.x < x_min) x_min = d.x;
if (d.y > y_max) y_max = d.y;
if (d.y < y_min) y_min = d.y;
});

// Size of the plot.
const width = y_max - y_min + options.node_x_size + options.margin * 2;
const height = x_max - x_min + options.node_y_size + options.margin * 2 +
options.node_y_offset - options.node_y_size;

const plot = d3.select(canvas_id);

// Tool tip
options.tooltip = plot.append(‘div’)
.attr(‘width’, 100)
.attr(‘height’, 100)
.style(‘padding’, ‘4px’)
.style(‘background’, ‘#fff’)
.style(‘box-shadow’, ‘4px 4px 0px rgba(0,0,0,0.1)’)
.style(‘border’, ‘1px solid black’)
.style(‘font-family’, ‘sans-serif’)
.style(‘font-size’, options.font_size)
.style(‘position’, ‘absolute’)
.style(‘z-index’, ‘10’)
.attr(‘pointer-events’, ‘none’)
.style(‘display’, ‘none’);

// Create canvas
const svg = plot.append(‘svg’).attr(‘width’, width).attr(‘height’, height);
const graph =
svg.style(‘overflow’, ‘visible’)
.append(‘g’)
.attr(‘font-family’, ‘sans-serif’)
.attr(‘font-size’, options.font_size)
.attr(
‘transform’,
() => translate(${options.margin},${ - x_min + options.node_y_offset / 2 + options.margin}));

// Plot bounding box.
if (options.show_plot_bounding_box) {
svg.append(‘rect’)
.attr(‘width’, width)
.attr(‘height’, height)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.0)
.attr(‘stroke’, ‘black’);
}

// Draw the edges.
display_edges(options, graph, tree_struct);

// Draw the nodes.
display_nodes(options, graph, tree_struct);
}

/**

Draw the nodes of the tree.
@param {!options} options Dictionary of configurations.
@param {!graph} graph D3 search handle containing the graph.
@param {!tree_struct} tree_struct Structure of the tree (node placement,
```
data, etc.).
```

*/
function display_nodes(options, graph, tree_struct) {
const nodes = graph.append(‘g’)
.selectAll(‘g’)
.data(tree_struct.descendants())
.join(‘g’)
.attr(‘transform’, d => translate(${d.y},${d.x}));

nodes.append(‘rect’)
.attr(‘x’, 0.5)
.attr(‘y’, 0.5)
.attr(‘width’, options.node_x_size)
.attr(‘height’, options.node_y_size)
.attr(‘stroke’, ‘lightgrey’)
.attr(‘stroke-width’, 1)
.attr(‘fill’, ‘white’)
.attr(‘y’, -options.node_y_size / 2);

// Brackets on the right of condition nodes without children.
non_leaf_node_without_children =
nodes.filter(node => node.data.condition != null && node.children == null)
.append(‘g’)
.attr(‘transform’, translate(${options.node_x_size},0));

non_leaf_node_without_children.append(‘path’)
.attr(‘d’, ‘M0,0 C 10,0 0,10 10,10’)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.0)
.attr(‘stroke’, ‘#F00’);

non_leaf_node_without_children.append(‘path’)
.attr(‘d’, ‘M0,0 C 10,0 0,-10 10,-10’)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.0)
.attr(‘stroke’, ‘#0F0’);

const node_content = nodes.append(‘g’).attr(
‘transform’,
translate(0,${options.node_padding - options.node_y_size / 2}));

node_content.append(node => create_node_element(options, node));
}

/**

Creates the D3 content for a single node.
@param {!options} options Dictionary of configurations.
@param {!node} node Node to draw.
@return {!d3} D3 content.
*/
function create_node_element(options, node) {
// Output accumulator.
let output = {
// Content to draw.
content: d3.create(‘svg:g’),
// Vertical offset to the next element to draw.
vertical_offset: 0
};

// Conditions.
if (node.data.condition != null) {
display_condition(options, node.data.condition, output);
}

// Values.
if (node.data.value != null) {
display_value(options, node.data.value, output);
}

// Explanations.
if (node.data.explanation != null) {
display_explanation(options, node.data.explanation, output);
}

return output.content.node();
}

/**

Adds a single line of text inside of a node.
@param {!options} options Dictionary of configurations.
@param {string} text Text to display.
@param {!output} output Output display accumulator.
*/
function display_node_text(options, text, output) {
output.content.append(‘text’)
.attr(‘x’, options.node_padding)
.attr(‘y’, output.vertical_offset)
.attr(‘alignment-baseline’, ‘hanging’)
.text(text);
output.vertical_offset += 10;
}

/**

Adds a single line of text inside of a node with a tooltip.
@param {!options} options Dictionary of configurations.
@param {string} text Text to display.
@param {string} tooltip Text in the Tooltip.
@param {!output} output Output display accumulator.
*/
function display_node_text_with_tooltip(options, text, tooltip, output) {
const item = output.content.append(‘text’)
.attr(‘x’, options.node_padding)
.attr(‘alignment-baseline’, ‘hanging’)
.text(text);

add_tooltip(options, item, () => tooltip);
output.vertical_offset += 10;
}

/**

Adds a tooltip to a dom element.
@param {!options} options Dictionary of configurations.
@param {!dom} target Dom element to equip with a tooltip.
@param {!func} get_content Generates the html content of the tooltip.
*/
function add_tooltip(options, target, get_content) {
function show(d) {
options.tooltip.style(‘display’, ‘block’);
options.tooltip.html(get_content());
}

function hide(d) {
options.tooltip.style(‘display’, ‘none’);
}

function move(d) {
options.tooltip.style(‘display’, ‘block’);
options.tooltip.style(‘left’, (d.pageX + 5) + ‘px’);
options.tooltip.style(‘top’, d.pageY + ‘px’);
}

target.on(‘mouseover’, show);
target.on(‘mouseout’, hide);
target.on(‘mousemove’, move);
}

/**

Adds a condition inside of a node.
@param {!options} options Dictionary of configurations.
@param {!condition} condition Condition to display.
@param {!output} output Output display accumulator.
*/
function display_condition(options, condition, output) {
threshold_format = d3.format(‘r’);

if (condition.type === ‘IS_MISSING’) {
display_node_text(options, ${condition.attribute} is missing, output);
return;
}

if (condition.type === ‘IS_TRUE’) {
display_node_text(options, ${condition.attribute} is true, output);
return;
}

if (condition.type === ‘NUMERICAL_IS_HIGHER_THAN’) {
format = d3.format(‘r’);
display_node_text(
options,
${condition.attribute} >= ${threshold_format(condition.threshold)},
output);
return;
}

if (condition.type === ‘CATEGORICAL_IS_IN’) {
display_node_text_with_tooltip(
options, ${condition.attribute} in [...],
${condition.attribute} in [${condition.mask}], output);
return;
}

if (condition.type === ‘CATEGORICAL_SET_CONTAINS’) {
display_node_text_with_tooltip(
options, ${condition.attribute} intersect [...],
${condition.attribute} intersect [${condition.mask}], output);
return;
}

if (condition.type === ‘NUMERICAL_SPARSE_OBLIQUE’) {
display_node_text_with_tooltip(
options, Sparse oblique split...,
[${condition.attributes}]*[${condition.weights}]>=${ threshold_format(condition.threshold)},
output);
return;
}

display_node_text(
options, Non supported condition ${condition.type}, output);
}

/**

Adds a value inside of a node.
@param {!options} options Dictionary of configurations.
@param {!value} value Value to display.
@param {!output} output Output display accumulator.
*/
function display_value(options, value, output) {
if (value.type === ‘PROBABILITY’) {
const left_margin = 0;
const right_margin = 50;
const plot_width = options.node_x_size - options.node_padding * 2 -
left_margin - right_margin;

let cusum = Array.from(d3.cumsum(value.distribution));
cusum.unshift(0);
const distribution_plot = output.content.append(‘g’).attr(
‘transform’, translate(0,${output.vertical_offset + 0.5}));

distribution_plot.selectAll(‘rect’)
.data(value.distribution)
.join(‘rect’)
.attr(‘height’, 10)
.attr(
‘x’,
(d, i) =>
(cusum[i] * plot_width + left_margin + options.node_padding))
.attr(‘width’, (d, i) => d * plot_width)
.style(‘fill’, (d, i) => d3.schemeSet1[i]);

const num_examples =
output.content.append(‘g’)
.attr(‘transform’, translate(0,${output.vertical_offset}))
.append(‘text’)
.attr(‘x’, options.node_x_size - options.node_padding)
.attr(‘alignment-baseline’, ‘hanging’)
.attr(‘text-anchor’, ‘end’)
.text((${value.num_examples}));

const distribution_details = d3.create(‘ul’);
distribution_details.selectAll(‘li’)
.data(value.distribution)
.join(‘li’)
.append(‘span’)
.text(
(d, i) =>
‘class ’ + i + ‘: ’ + d3.format(’.3%’)(value.distribution[i]));

add_tooltip(options, distribution_plot, () => distribution_details.html());
add_tooltip(options, num_examples, () => ‘Number of examples’);

output.vertical_offset += 10;
return;
}

if (value.type === ‘REGRESSION’) {
display_node_text(
options,
‘value: ’ + d3.format(‘r’)(value.value) + ( +
d3.format(’.6’)(value.num_examples) + ),
output);
return;
}

display_node_text(options, Non supported value ${value.type}, output);
}

/**

Adds an explanation inside of a node.
@param {!options} options Dictionary of configurations.
@param {!explanation} explanation Explanation to display.
@param {!output} output Output display accumulator.
*/
function display_explanation(options, explanation, output) {
// Margin before the explanation.
output.vertical_offset += 10;

display_node_text(
options, Non supported explanation ${explanation.type}, output);
}

/**

Draw the edges of the tree.
@param {!options} options Dictionary of configurations.
@param {!graph} graph D3 search handle containing the graph.
@param {!tree_struct} tree_struct Structure of the tree (node placement,
```
data, etc.).
```

*/
function display_edges(options, graph, tree_struct) {
// Draw an edge between a parent and a child node with a bezier.
function draw_single_edge(d) {
return ‘M’ + (d.source.y + options.node_x_size) + ‘,’ + d.source.x + ’ C’ +
(d.source.y + options.node_x_size + options.edge_rounding) + ‘,’ +
d.source.x + ’ ’ + (d.target.y - options.edge_rounding) + ‘,’ +
d.target.x + ’ ’ + d.target.y + ‘,’ + d.target.x;
}

graph.append(‘g’)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.2)
.selectAll(‘path’)
.data(tree_struct.links())
.join(‘path’)
.attr(‘d’, draw_single_edge)
.attr(
‘stroke’, d => (d.target === d.source.children[0]) ? ‘#0F0’ : ‘#F00’);
}

display_tree({“margin”: 10, “node_x_size”: 160, “node_y_size”: 28, “node_x_offset”: 180, “node_y_offset”: 33, “font_size”: 10, “edge_rounding”: 20, “node_padding”: 2, “show_plot_bounding_box”: false}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.47093023255813954, 0.19476744186046513, 0.33430232558139533], “num_examples”: 344.0}, “condition”: {“type”: “NUMERICAL_IS_HIGHER_THAN”, “attribute”: “bill_length_mm”, “threshold”: 43.25}, “children”: [{“value”: {“type”: “PROBABILITY”, “distribution”: [0.005847953216374269, 0.3567251461988304, 0.6374269005847953], “num_examples”: 171.0}, “condition”: {“type”: “CATEGORICAL_IS_IN”, “attribute”: “island”, “mask”: [“Biscoe”]}, “children”: [{“value”: {“type”: “PROBABILITY”, “distribution”: [0.00909090909090909, 0.0, 0.990909090909091], “num_examples”: 110.0}, “condition”: {“type”: “NUMERICAL_IS_HIGHER_THAN”, “attribute”: “bill_depth_mm”, “threshold”: 17.225584030151367}, “children”: [{“value”: {“type”: “PROBABILITY”, “distribution”: [0.16666666666666666, 0.0, 0.8333333333333334], “num_examples”: 6.0}}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.0, 0.0, 1.0], “num_examples”: 104.0}}]}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.0, 1.0, 0.0], “num_examples”: 61.0}}]}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.930635838150289, 0.03468208092485549, 0.03468208092485549], “num_examples”: 173.0}, “condition”: {“type”: “NUMERICAL_IS_HIGHER_THAN”, “attribute”: “bill_depth_mm”, “threshold”: 15.100000381469727}, “children”: [{“value”: {“type”: “PROBABILITY”, “distribution”: [0.9640718562874252, 0.03592814371257485, 0.0], “num_examples”: 167.0}, “condition”: {“type”: “NUMERICAL_IS_HIGHER_THAN”, “attribute”: “flipper_length_mm”, “threshold”: 187.5}, “children”: [{“value”: {“type”: “PROBABILITY”, “distribution”: [1.0, 0.0, 0.0], “num_examples”: 104.0}}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.9047619047619048, 0.09523809523809523, 0.0], “num_examples”: 63.0}, “condition”: {“type”: “NUMERICAL_IS_HIGHER_THAN”, “attribute”: “bill_length_mm”, “threshold”: 42.30000305175781}}]}, {“value”: {“type”: “PROBABILITY”, “distribution”: [0.0, 0.0, 1.0], “num_examples”: 6.0}}]}]}, “#tree_plot_05707b35c4f748738efd3da21ab9197f”)

Check model structure

Model structure and metadata can be obtained through the make_inspector()created inspector .

**Note:** Depending on the learning algorithm and hyperparameters, the inspector will expose different specialized properties. For example, winner_take_allfields are specific to the Random Forest model.

# 创建一个模型检查器对象，用于检查模型的性能和质量
inspector = model.make_inspector()

For our model, the available inspector fields are:

# 使用列表推导式，遍历inspector模块中的所有属性
# 过滤掉以"_"开头的属性
fields = [field for field in dir(inspector) if not field.startswith("_")]

['MODEL_NAME',
 'dataspec',
 'evaluation',
 'export_to_tensorboard',
 'extract_all_trees',
 'extract_tree',
 'features',
 'header',
 'iterate_on_nodes',
 'label',
 'label_classes',
 'metadata',
 'model_type',
 'num_trees',
 'objective',
 'specialized_header',
 'task',
 'training_logs',
 'tuning_logs',
 'variable_importances',
 'winner_take_all_inference']

Remember to check out the API reference or use ?View built-in documentation.

?inspector.model_type

Some model metadata:

# 打印模型类型
print("Model type:", inspector.model_type())

# 打印模型中树的数量
print("Number of trees:", inspector.num_trees())

# 打印模型的目标函数
print("Objective:", inspector.objective())

# 打印模型的输入特征
print("Input features:", inspector.features())

Model type: RANDOM_FOREST
Number of trees: 300
Objective: Classification(label=__LABEL, class=None, num_classes=3)
Input features: ["bill_depth_mm" (1; #0), "bill_length_mm" (1; #1), "body_mass_g" (1; #2), "flipper_length_mm" (1; #3), "island" (4; #4), "sex" (4; #5), "year" (1; #6)]

evaluate()is the model evaluation calculated during training. The dataset used for this evaluation depends on the algorithm. For example, it can be a validation dataset or an out-of-bag dataset.

**Note:** Although calculated during training, it evaluate()is never evaluated on the training data set.

# 创建一个名为inspector的对象
inspector = Inspector()
# 调用inspector对象的evaluation()方法
inspector.evaluation()

Evaluation(num_examples=344, accuracy=0.9767441860465116, loss=0.06782230959804512, rmse=None, ndcg=None, aucs=None, auuc=None, qini=None)

The importance of variables is as follows:

The variable importances are:

# 打印可用的变量重要性
print(f"Available variable importances:")

# 遍历变量重要性字典的键，并打印出来
for importance in inspector.variable_importances().keys():
    print("\t", importance)

Available variable importances:
	 MEAN_DECREASE_IN_AP_1_VS_OTHERS
	 MEAN_DECREASE_IN_PRAUC_3_VS_OTHERS
	 SUM_SCORE
	 MEAN_DECREASE_IN_PRAUC_1_VS_OTHERS
	 MEAN_DECREASE_IN_ACCURACY
	 MEAN_DECREASE_IN_AUC_1_VS_OTHERS
	 MEAN_DECREASE_IN_AP_3_VS_OTHERS
	 NUM_AS_ROOT
	 MEAN_DECREASE_IN_AP_2_VS_OTHERS
	 MEAN_DECREASE_IN_AUC_2_VS_OTHERS
	 MEAN_MIN_DEPTH
	 MEAN_DECREASE_IN_AUC_3_VS_OTHERS
	 NUM_NODES
	 MEAN_DECREASE_IN_PRAUC_2_VS_OTHERS

Different variable importances have different semantics. For example, a feature with an average reduced AUC0.05 means that removing the feature from the training dataset reduces/impairs the AUC by 5%.

# 获取类别1与其他类别之间的AUC的平均减少量
mean_decrease_in_auc_1_vs_others = inspector.variable_importances()["MEAN_DECREASE_IN_AUC_1_VS_OTHERS"]

[("bill_length_mm" (1; #1), 0.0713061951754389),
 ("island" (4; #4), 0.007298519736842035),
 ("flipper_length_mm" (1; #3), 0.004505893640351366),
 ("bill_depth_mm" (1; #0), 0.0021244517543865804),
 ("body_mass_g" (1; #2), 0.0005482456140351033),
 ("sex" (4; #5), 0.00047971491228060437),
 ("year" (1; #6), 0.0)]

Plot variable importance in inspector using Matplotlib

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 4))  # 创建一个大小为12x4的图形

# 平均AUC下降值（class 1相对于其他类别）
variable_importance_metric = "MEAN_DECREASE_IN_AUC_1_VS_OTHERS"
variable_importances = inspector.variable_importances()[variable_importance_metric]

# 提取特征名称和重要性值
#
# `variable_importances` 是一个包含<特征, 重要性>元组的列表
feature_names = [vi[0].name for vi in variable_importances]  # 提取特征名称
feature_importances = [vi[1] for vi in variable_importances]  # 提取重要性值
# 特征按重要性值降序排列
feature_ranks = range(len(feature_names))

bar = plt.barh(feature_ranks, feature_importances, label=[str(x) for x in feature_ranks])  # 创建水平条形图
plt.yticks(feature_ranks, feature_names)  # 设置y轴刻度为特征名称
plt.gca().invert_yaxis()  # 反转y轴刻度顺序，使重要性高的特征在上方

# TODO: 当可用时，替换为 "plt.bar_label()"
# 使用值标记每个条形图
for importance, patch in zip(feature_importances, bar.patches):
  plt.text(patch.get_x() + patch.get_width(), patch.get_y(), f"{
      
      importance:.4f}", va="top")

plt.xlabel(variable_importance_metric)  # 设置x轴标签为重要性度量
plt.title("Mean decrease in AUC of the class 1 vs the others")  # 设置图形标题
plt.tight_layout()  # 调整图形布局，以防止标签重叠
plt.show()  # 显示图形

Finally, access the actual tree structure:

# 从inspector对象中提取树的信息
# 参数tree_idx表示要提取的树的索引，这里为0表示提取第一棵树的信息
inspector.extract_tree(tree_idx=0)

Tree(root=NonLeafNode(condition=(bill_length_mm >= 43.25; miss=True, score=0.5482327342033386), pos_child=NonLeafNode(condition=(island in ['Biscoe']; miss=True, score=0.6515106558799744), pos_child=NonLeafNode(condition=(bill_depth_mm >= 17.225584030151367; miss=False, score=0.027205035090446472), pos_child=LeafNode(value=ProbabilityValue([0.16666666666666666, 0.0, 0.8333333333333334],n=6.0), idx=7), neg_child=LeafNode(value=ProbabilityValue([0.0, 0.0, 1.0],n=104.0), idx=6), value=ProbabilityValue([0.00909090909090909, 0.0, 0.990909090909091],n=110.0)), neg_child=LeafNode(value=ProbabilityValue([0.0, 1.0, 0.0],n=61.0), idx=5), value=ProbabilityValue([0.005847953216374269, 0.3567251461988304, 0.6374269005847953],n=171.0)), neg_child=NonLeafNode(condition=(bill_depth_mm >= 15.100000381469727; miss=True, score=0.150658518075943), pos_child=NonLeafNode(condition=(flipper_length_mm >= 187.5; miss=True, score=0.036139510571956635), pos_child=LeafNode(value=ProbabilityValue([1.0, 0.0, 0.0],n=104.0), idx=4), neg_child=NonLeafNode(condition=(bill_length_mm >= 42.30000305175781; miss=True, score=0.23430533707141876), pos_child=LeafNode(value=ProbabilityValue([0.0, 1.0, 0.0],n=5.0), idx=3), neg_child=NonLeafNode(condition=(bill_length_mm >= 40.55000305175781; miss=True, score=0.043961383402347565), pos_child=LeafNode(value=ProbabilityValue([0.8, 0.2, 0.0],n=5.0), idx=2), neg_child=LeafNode(value=ProbabilityValue([1.0, 0.0, 0.0],n=53.0), idx=1), value=ProbabilityValue([0.9827586206896551, 0.017241379310344827, 0.0],n=58.0)), value=ProbabilityValue([0.9047619047619048, 0.09523809523809523, 0.0],n=63.0)), value=ProbabilityValue([0.9640718562874252, 0.03592814371257485, 0.0],n=167.0)), neg_child=LeafNode(value=ProbabilityValue([0.0, 0.0, 1.0],n=6.0), idx=0), value=ProbabilityValue([0.930635838150289, 0.03468208092485549, 0.03468208092485549],n=173.0)), value=ProbabilityValue([0.47093023255813954, 0.19476744186046513, 0.33430232558139533],n=344.0)), label_classes=None)

Extracting trees is not efficient. If speed is important, you can use iterate_on_nodes()methods for model checking. This method is a depth-first preorder traversal iterator over all nodes of the model.

Note:extract_tree() It is iterate_on_nodes()implemented using .

The following example counts the number of times each feature is used (an indicator of the importance of a structural variable):

# 创建一个默认字典number_of_use，用于记录每个特征在其条件中被使用的次数
number_of_use = collections.defaultdict(lambda: 0)

# 对所有节点进行深度优先的前序遍历
for node_iter in inspector.iterate_on_nodes():

  # 如果节点不是叶节点，则跳过
  if not isinstance(node_iter.node, tfdf.py_tree.node.NonLeafNode):
    continue

  # 遍历节点条件中使用的所有特征
  # 默认情况下，模型是"oblique"的，即每个节点测试一个特征
  for feature in node_iter.node.condition.features():
    # 特征在使用次数上加1
    number_of_use[feature] += 1

# 打印每个特征的条件节点数
print("Number of condition nodes per features:")
for feature, count in number_of_use.items():
  print("\t", feature.name, ":", count)

Number of condition nodes per features:
	 bill_length_mm : 778
	 bill_depth_mm : 463
	 flipper_length_mm : 414
	 island : 342
	 body_mass_g : 338
	 year : 19
	 sex : 36

Create a model manually

In this section, you will manually create a small random forest model. To make it even simpler, the model only contains a simple tree:

3个标签类别：红色、蓝色和绿色。
2个特征：f1（数值型）和f2（字符串分类型）

f1>=1.5
    ├─(正)─ f2在["猫","狗"]中
    │         ├─(正)─ 值：[0.8, 0.1, 0.1]
    │         └─(负)─ 值：[0.1, 0.8, 0.1]
    └─(负)─ 值：[0.1, 0.1, 0.8]

# 创建模型构建器
builder = tfdf.builder.RandomForestBuilder(
    path="/tmp/manual_model",  # 指定模型保存的路径
    objective=tfdf.py_tree.objective.ClassificationObjective(
        label="color",  # 指定目标变量为"color"
        classes=["red", "blue", "green"]))  # 指定目标变量的类别为["red", "blue", "green"]

Each tree is added one by one.

Note: The tree object ( ) is the same as the tree object returned tfdf.py_tree.tree.Treein the previous section .extract_tree()

# 导入所需的模块和类
Tree = tfdf.py_tree.tree.Tree  # 树结构
SimpleColumnSpec = tfdf.py_tree.dataspec.SimpleColumnSpec  # 列规范
ColumnType = tfdf.py_tree.dataspec.ColumnType  # 列类型
NonLeafNode = tfdf.py_tree.node.NonLeafNode  # 非叶节点
LeafNode = tfdf.py_tree.node.LeafNode  # 叶节点
NumericalHigherThanCondition = tfdf.py_tree.condition.NumericalHigherThanCondition  # 数值大于条件
CategoricalIsInCondition = tfdf.py_tree.condition.CategoricalIsInCondition  # 类别在条件
ProbabilityValue = tfdf.py_tree.value.ProbabilityValue  # 概率值

# 创建树结构并添加到builder中
builder.add_tree(
    Tree(
        NonLeafNode(
            condition=NumericalHigherThanCondition(
                feature=SimpleColumnSpec(name="f1", type=ColumnType.NUMERICAL),  # 数值特征"f1"
                threshold=1.5,  # 阈值为1.5
                missing_evaluation=False),  # 不考虑缺失值
            pos_child=NonLeafNode(
                condition=CategoricalIsInCondition(
                    feature=SimpleColumnSpec(name="f2",type=ColumnType.CATEGORICAL),  # 类别特征"f2"
                    mask=["cat", "dog"],  # 类别为"cat"或"dog"
                    missing_evaluation=False),  # 不考虑缺失值
                pos_child=LeafNode(value=ProbabilityValue(probability=[0.8, 0.1, 0.1], num_examples=10)),  # 正向子节点为叶节点，概率值为[0.8, 0.1, 0.1]，样本数为10
                neg_child=LeafNode(value=ProbabilityValue(probability=[0.1, 0.8, 0.1], num_examples=20))),  # 负向子节点为叶节点，概率值为[0.1, 0.8, 0.1]，样本数为20
            neg_child=LeafNode(value=ProbabilityValue(probability=[0.1, 0.1, 0.8], num_examples=30)))))  # 负向子节点为叶节点，概率值为[0.1, 0.1, 0.8]，样本数为30

end tree writing

# 关闭builder对象
builder.close()

[INFO 2022-12-14T12:25:00.790486355+00:00 kernel.cc:1175] Loading model from path /tmp/manual_model/tmp/ with prefix e09a067144bc479b
[INFO 2022-12-14T12:25:00.790802259+00:00 decision_forest.cc:640] Model loaded with 1 root(s), 5 node(s), and 2 input feature(s).
[INFO 2022-12-14T12:25:00.790878962+00:00 kernel.cc:1021] Use fast generic engine
WARNING:absl:Found untraced functions such as call_get_leaves, _update_step_xla while saving (showing 2 of 2). These functions will not be directly callable after loading.


INFO:tensorflow:Assets written to: /tmp/manual_model/assets


INFO:tensorflow:Assets written to: /tmp/manual_model/assets

Now you can open the model as a regular keras model and make predictions:

# 加载预训练模型
manual_model = tf.keras.models.load_model("/tmp/manual_model")

[INFO 2022-12-14T12:25:01.436506097+00:00 kernel.cc:1175] Loading model from path /tmp/manual_model/assets/ with prefix e09a067144bc479b
[INFO 2022-12-14T12:25:01.436871761+00:00 decision_forest.cc:640] Model loaded with 1 root(s), 5 node(s), and 2 input feature(s).
[INFO 2022-12-14T12:25:01.436909696+00:00 kernel.cc:1021] Use fast generic engine

# 创建一个tf.data.Dataset对象，从给定的张量中切片得到数据集
# 数据集包含两个特征"f1"和"f2"，分别是浮点数和字符串类型
# 数据集中的每个样本是一个字典，包含"f1"和"f2"两个键
# 样本数据为：
#   "f1": [1.0, 2.0, 3.0]
#   "f2": ["cat", "cat", "bird"]
# 使用batch(2)方法将数据集划分为大小为2的批次
examples = tf.data.Dataset.from_tensor_slices({
    
    
    "f1": [1.0, 2.0, 3.0],
    "f2": ["cat", "cat", "bird"]
}).batch(2)

# 使用manual_model对examples进行预测
predictions = manual_model.predict(examples)

# 打印预测结果
print("predictions:\n", predictions)

1/2 [==============>...............] - ETA: 0s
2/2 [==============================] - 0s 2ms/step
predictions:
 [[0.1 0.1 0.8]
 [0.8 0.1 0.1]
 [0.1 0.8 0.1]]

Access structure:

NOTE: Since the model is serialized and deserialized, you need to use an alternative but equivalent form.

# 代码注释

# 获取yggdrasil模型路径
yggdrasil_model_path = manual_model.yggdrasil_model_path_tensor().numpy().decode("utf-8")
print("yggdrasil_model_path:",yggdrasil_model_path)

# 创建一个模型检查器，用于检查模型的输入特征
inspector = tfdf.inspector.make_inspector(yggdrasil_model_path)
print("Input features:", inspector.features())

yggdrasil_model_path: /tmp/manual_model/assets/
Input features: ["f1" (1; #1), "f2" (4; #2)]

Of course, you can draw this constructed model manually:

# 导入tfdf库中的plot_model_in_colab函数
import tensorflow_decision_forests as tfdf

# 使用plot_model_in_colab函数绘制manual_model模型的结构图
tfdf.model_plotter.plot_model_in_colab(manual_model)

/**

Plotting of decision trees generated by TF-DF.
A tree is a recursive structure of node objects.
A node contains one or more of the following components:
- A value: Representing the output of the node. If the node is not a leaf,

 the value is only present for analysis i.e. it is not used for

```
 predictions.
```
- A condition : For non-leaf nodes, the condition (also known as split)

 defines a binary test to branch to the positive or negative child.

- An explanation: Generally a plot showing the relation between the label

 and the condition to give insights about the effect of the condition.

- Two children : For non-leaf nodes, the children nodes. The first

 children (i.e. "node.children[0]") is the negative children (drawn in

 red). The second children is the positive one (drawn in green).

/**

Plots a single decision tree into a DOM element.
@param {!options} options Dictionary of configurations.
@param {!tree} raw_tree Recursive tree structure.
@param {string} canvas_id Id of the output dom element.
*/
function display_tree(options, raw_tree, canvas_id) {
console.log(options);

// Determine the node placement.
const tree_struct = d3.tree().nodeSize(
[options.node_y_offset, options.node_x_offset])(d3.hierarchy(raw_tree));

// Boundaries of the node placement.
let x_min = Infinity;
let x_max = -x_min;
let y_min = Infinity;
let y_max = -x_min;

tree_struct.each(d => {
if (d.x > x_max) x_max = d.x;
if (d.x < x_min) x_min = d.x;
if (d.y > y_max) y_max = d.y;
if (d.y < y_min) y_min = d.y;
});

const plot = d3.select(canvas_id);

// Draw the edges.
display_edges(options, graph, tree_struct);

// Draw the nodes.
display_nodes(options, graph, tree_struct);
}

/**

Draw the nodes of the tree.
@param {!options} options Dictionary of configurations.
@param {!graph} graph D3 search handle containing the graph.
@param {!tree_struct} tree_struct Structure of the tree (node placement,
```
data, etc.).
```

non_leaf_node_without_children.append(‘path’)
.attr(‘d’, ‘M0,0 C 10,0 0,10 10,10’)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.0)
.attr(‘stroke’, ‘#F00’);

non_leaf_node_without_children.append(‘path’)
.attr(‘d’, ‘M0,0 C 10,0 0,-10 10,-10’)
.attr(‘fill’, ‘none’)
.attr(‘stroke-width’, 1.0)
.attr(‘stroke’, ‘#0F0’);

const node_content = nodes.append(‘g’).attr(
‘transform’,
translate(0,${options.node_padding - options.node_y_size / 2}));

node_content.append(node => create_node_element(options, node));
}

/**

Creates the D3 content for a single node.
@param {!options} options Dictionary of configurations.
@param {!node} node Node to draw.
@return {!d3} D3 content.
*/
function create_node_element(options, node) {
// Output accumulator.
let output = {
// Content to draw.
content: d3.create(‘svg:g’),
// Vertical offset to the next element to draw.
vertical_offset: 0
};

// Conditions.
if (node.data.condition != null) {
display_condition(options, node.data.condition, output);
}

// Values.
if (node.data.value != null) {
display_value(options, node.data.value, output);
}

// Explanations.
if (node.data.explanation != null) {
display_explanation(options, node.data.explanation, output);
}

return output.content.node();
}

/**

Adds a single line of text inside of a node.
@param {!options} options Dictionary of configurations.
@param {string} text Text to display.
@param {!output} output Output display accumulator.
*/
function display_node_text(options, text, output) {
output.content.append(‘text’)
.attr(‘x’, options.node_padding)
.attr(‘y’, output.vertical_offset)
.attr(‘alignment-baseline’, ‘hanging’)
.text(text);
output.vertical_offset += 10;
}

/**

Adds a single line of text inside of a node with a tooltip.
@param {!options} options Dictionary of configurations.
@param {string} text Text to display.
@param {string} tooltip Text in the Tooltip.
@param {!output} output Output display accumulator.
*/
function display_node_text_with_tooltip(options, text, tooltip, output) {
const item = output.content.append(‘text’)
.attr(‘x’, options.node_padding)
.attr(‘alignment-baseline’, ‘hanging’)
.text(text);

add_tooltip(options, item, () => tooltip);
output.vertical_offset += 10;
}

/**

Adds a tooltip to a dom element.
@param {!options} options Dictionary of configurations.
@param {!dom} target Dom element to equip with a tooltip.
@param {!func} get_content Generates the html content of the tooltip.
*/
function add_tooltip(options, target, get_content) {
function show(d) {
options.tooltip.style(‘display’, ‘block’);
options.tooltip.html(get_content());
}

function hide(d) {
options.tooltip.style(‘display’, ‘none’);
}

function move(d) {
options.tooltip.style(‘display’, ‘block’);
options.tooltip.style(‘left’, (d.pageX + 5) + ‘px’);
options.tooltip.style(‘top’, d.pageY + ‘px’);
}

target.on(‘mouseover’, show);
target.on(‘mouseout’, hide);
target.on(‘mousemove’, move);
}

/**

Adds a condition inside of a node.
@param {!options} options Dictionary of configurations.
@param {!condition} condition Condition to display.
@param {!output} output Output display accumulator.
*/
function display_condition(options, condition, output) {
threshold_format = d3.format(‘r’);

if (condition.type === ‘IS_MISSING’) {
display_node_text(options, ${condition.attribute} is missing, output);
return;
}

if (condition.type === ‘IS_TRUE’) {
display_node_text(options, ${condition.attribute} is true, output);
return;
}

if (condition.type === ‘CATEGORICAL_IS_IN’) {
display_node_text_with_tooltip(
options, ${condition.attribute} in [...],
${condition.attribute} in [${condition.mask}], output);
return;
}

display_node_text(
options, Non supported condition ${condition.type}, output);
}

/**

Adds a value inside of a node.
@param {!options} options Dictionary of configurations.
@param {!value} value Value to display.
@param {!output} output Output display accumulator.
*/
function display_value(options, value, output) {
if (value.type === ‘PROBABILITY’) {
const left_margin = 0;
const right_margin = 50;
const plot_width = options.node_x_size - options.node_padding * 2 -
left_margin - right_margin;

let cusum = Array.from(d3.cumsum(value.distribution));
cusum.unshift(0);
const distribution_plot = output.content.append(‘g’).attr(
‘transform’, translate(0,${output.vertical_offset + 0.5}));

distribution_plot.selectAll(‘rect’)
.data(value.distribution)
.join(‘rect’)
.attr(‘height’, 10)
.attr(
‘x’,
(d, i) =>
(cusum[i] * plot_width + left_margin + options.node_padding))
.attr(‘width’, (d, i) => d * plot_width)
.style(‘fill’, (d, i) => d3.schemeSet1[i]);

const num_examples =
output.content.append(‘g’)
.attr(‘transform’, translate(0,${output.vertical_offset}))
.append(‘text’)
.attr(‘x’, options.node_x_size - options.node_padding)
.attr(‘alignment-baseline’, ‘hanging’)
.attr(‘text-anchor’, ‘end’)
.text((${value.num_examples}));

const distribution_details = d3.create(‘ul’);
distribution_details.selectAll(‘li’)
.data(value.distribution)
.join(‘li’)
.append(‘span’)
.text(
(d, i) =>
‘class ’ + i + ‘: ’ + d3.format(’.3%’)(value.distribution[i]));

add_tooltip(options, distribution_plot, () => distribution_details.html());
add_tooltip(options, num_examples, () => ‘Number of examples’);

output.vertical_offset += 10;
return;
}

if (value.type === ‘REGRESSION’) {
display_node_text(
options,
‘value: ’ + d3.format(‘r’)(value.value) + ( +
d3.format(’.6’)(value.num_examples) + ),
output);
return;
}

display_node_text(options, Non supported value ${value.type}, output);
}

/**

Adds an explanation inside of a node.
@param {!options} options Dictionary of configurations.
@param {!explanation} explanation Explanation to display.
@param {!output} output Output display accumulator.
*/
function display_explanation(options, explanation, output) {
// Margin before the explanation.
output.vertical_offset += 10;

display_node_text(
options, Non supported explanation ${explanation.type}, output);
}

/**

Draw the edges of the tree.
@param {!options} options Dictionary of configurations.
@param {!graph} graph D3 search handle containing the graph.
@param {!tree_struct} tree_struct Structure of the tree (node placement,
```
data, etc.).
```

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