AI 助理
备案控制台
文档
输入文档关键字查找
首页 日志服务 操作指南 查询与分析 通过索引模式查询和分析日志 SQL分析语法与功能 SQL函数 AI机器学习函数 分类判别分析函数（Classification）

分类判别分析函数（Classification）

更新时间：
产品详情
我的收藏

分类判别机器学习模型可以用于在线识别或者判别系统对象的分类，比如识别每一个用户的请求是否为攻击性的请求。也可以用于识别因素之间的关联关系。本文介绍分类判别分析函数的基本语法和示例。

背景信息

  • 本文介绍的分类判别函数示例索引如图所示。更多信息，请参见创建索引。

    image

  • 日志样例如下：

    1,Male,27,Software Engineer,6.1,6,42,6,Overweight,126,83,77,4200,None
2,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None
3,Male,28,Doctor,6.2,6,60,8,Normal,125,80,75,10000,None
4,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea
5,Male,28,Sales Representative,5.9,4,30,8,Obese,140,90,85,3000,Sleep Apnea
6,Male,28,Software Engineer,5.9,4,30,8,Obese,140,90,85,3000,Insomnia
7,Male,29,Teacher,6.3,6,40,7,Obese,140,90,82,3500,Insomnia
8,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
9,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
10,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
11,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None
12,Male,29,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
13,Male,29,Doctor,6.1,6,30,8,Normal,120,80,70,8000,None
14,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
15,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
16,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,None
17,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Sleep Apnea
18,Male,29,Doctor,6,6,30,8,Normal,120,80,70,8000,Sleep Apnea
19,Female,29,Nurse,6.5,5,40,7,Normal Weight,132,87,80,4000,Insomnia
20,Male,30,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
21,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
22,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
23,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
24,Male,30,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
25,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
26,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
27,Male,30,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
28,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
29,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
30,Male,30,Doctor,7.9,7,75,6,Normal,120,80,70,8000,None
31,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Sleep Apnea
32,Female,30,Nurse,6.4,5,35,7,Normal Weight,130,86,78,4100,Insomnia
33,Female,31,Nurse,7.9,8,75,4,Normal Weight,117,76,69,6800,None
34,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
35,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
36,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
37,Male,31,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
38,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
39,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
40,Male,31,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
41,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
42,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
43,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
44,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
45,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
46,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
47,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
48,Male,31,Doctor,7.8,7,75,6,Normal,120,80,70,8000,None
49,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
50,Male,31,Doctor,7.7,7,75,6,Normal,120,80,70,8000,Sleep Apnea
51,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None
52,Male,32,Engineer,7.5,8,45,3,Normal,120,80,70,8000,None
53,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
54,Male,32,Doctor,7.6,7,75,6,Normal,120,80,70,8000,None
55,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
56,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
57,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
58,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
59,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
60,Male,32,Doctor,7.7,7,75,6,Normal,120,80,70,8000,None
61,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
62,Male,32,Doctor,6,6,30,8,Normal,125,80,72,5000,None
63,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
64,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
65,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
66,Male,32,Doctor,6.2,6,30,8,Normal,125,80,72,5000,None
67,Male,32,Accountant,7.2,8,50,6,Normal Weight,118,76,68,7000,None
68,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,Insomnia
69,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None
70,Female,33,Scientist,6.2,6,50,6,Overweight,128,85,76,5500,None
71,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
72,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
73,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
74,Male,33,Doctor,6.1,6,30,8,Normal,125,80,72,5000,None
75,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
76,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
77,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
78,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
79,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
80,Male,33,Doctor,6,6,30,8,Normal,125,80,72,5000,None
81,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea
82,Female,34,Scientist,5.8,4,32,8,Overweight,131,86,81,5200,Sleep Apnea
83,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None
84,Male,35,Teacher,6.7,7,40,5,Overweight,128,84,70,5600,None
85,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None
86,Female,35,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None
87,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None
88,Male,35,Engineer,7.2,8,60,4,Normal,125,80,65,5000,None
89,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
90,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
91,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
92,Male,35,Engineer,7.3,8,60,4,Normal,125,80,65,5000,None
93,Male,35,Software Engineer,7.5,8,60,5,Normal Weight,120,80,70,8000,None
94,Male,35,Lawyer,7.4,7,60,5,Obese,135,88,84,3300,Sleep Apnea
95,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,Insomnia
96,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None
97,Female,36,Accountant,7.2,8,60,4,Normal,115,75,68,7000,None
98,Female,36,Accountant,7.1,8,60,4,Normal,115,75,68,7000,None
99,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None
100,Female,36,Teacher,7.1,8,60,4,Normal,115,75,68,7000,None
101,Female,36,Teacher,7.2,8,60,4,Normal,115,75,68,7000,None

分类判别分析函数列表

分类判别分析函数使用分类判别机器学习模型，分类判别机器学习模型可以用于在线识别或者判别系统对象的分类。

函数名称

语法

说明

返回值类型

decision_tree_classifier函数

decision_tree_classifier(

target_variable varchar,

input_variable_array array(varchar),

target_variable_name varchar,

input_variable_name_array array(varchar),

input_variable_type_array array(varchar),

<optional> model_options varchar

)

根据最近输入的一批数据样本，训练出可用于分类和原因分析的决策树模型。

varchar

decision_tree_predict函数

decision_tree_predict(

decision_tree_model_in_json varchar,

input_variable_array array(varchar)

)

结合decision_tree_classifier函数识别出来的决策树模型和给定的输入数据样本，判别系统对象属于哪一种分类。

varchar

decision_tree_classifier函数

根据最近输入的一批数据样本，训练出可用于分类和原因分析的决策树模型。

varchar decision_tree_classifier(target_variable varchar,input_variable_array array(varchar),target_variable_name varchar,input_variable_name_array array(varchar),input_variable_type_array array(varchar),<optional> model_options varchar)

参数

说明

target_variable

作为目标变量的字段。

input_variable_array

输入变量形成的数组，把输入变量转换成为字符类型后组成的一维数组。

target_variable_name

目标变量的名称。

input_variable_name_array

输入变量名称组成的数组。

input_variable_type_array

输入变量类型组成的数组。

输入变量类型：

  • ID_STR：对应字段是分类对象的字符串类型的ID。

  • ID_NUM：对应字段是分类对象的数字类型的ID。

  • X_STR_CATEGORICAL：对应字段是字符串类型的离散型数据，例如某一字段的值仅限于'Male'、'Female'或'Unknown'这几个固定选项。

  • X_NUM_CATEGORICAL：对应字段是离散型的数据，但是数据类型是数字离散型，例如某一字段的值仅限于1、2、3、4、5这几个固定选项。

  • X_NUMERIC：对应字段是连续的数字类型数据。

model_options

通过键值对（key=value）组成，多个键值对使用逗号或分号进行分隔（决策树模型的高级参数，通常无需设置）。例如：criterion=gini,splitter=best,max_depth=100,min_samples_split=20,min_samples_leaf=10,min_weight_fraction_leaf=0.01,max_leaf_nodes=100000,min_impurity_decrease=0.01,class_weight=balanced。

决策树模型高级参数：

  • criterion：分裂时选用的依据。选值为gini、 entropy或者log_loss。

  • splitter：分裂每个节点选用的策略。选值为best或者random。

  • max_depth：决策树的最大深度。

  • min_samples_split：分裂一个节点时，需要的数据最小样本个数，才能进一步分裂。

  • min_samples_leaf：叶子节点需要的最小样本数量。

  • min_weight_fraction_leaf：样本权重占总体样本权重的百分比的最小比例。

  • max_leaf_nodes：最大叶子节点数量。

  • min_impurity_decrease：节点分裂时，非纯度需要下降多少才进行分裂。

  • class_weight：类别的权重。在处理样本不平衡问题时，采用balanced选项可使样本权重与各类样本数量成反比，即样本量越大，单个样本的权重越低。

使用示例

  • 查询和分析语句

    * | with sleep_health_group_data as
(
  select g.group_id,
  s.*
  from (
    select 'G1' as group_id union all
    select 'G2' as group_id
  ) as g         -- 增加一个group_id的字段用于展示决策树模型识别是一个聚合函数
  cross join log as s
)

select group_id,
    decision_tree_classifier(
      sleep_disorder,
      array[cast(person_id as varchar),
              cast(gender as varchar),
              cast(age as varchar),
              cast(occupation as varchar),
              cast(sleep_duration as varchar),
              cast(quality_of_sleep as varchar),
              cast(physical_activity_level as varchar),
              cast(stress_level as varchar),
              cast(bmi_category as varchar),
              cast(blood_pressure_systolic as varchar),
              cast(blood_pressure_diastolic as varchar),
              cast(heart_rate as varchar),
              cast(daily_steps as varchar)],
      'sleep_disorder',
      array['person_id', 'gender', 'age', 'occupation', 'sleep_duration',
              'quality_of_sleep', 'physical_activity_level', 'stress_level',
              'bmi_category', 'blood_pressure_systolic', 'blood_pressure_diastolic',
              'heart_rate', 'daily_steps'],
      array['ID_NUM',
              'X_STR_CATEGORICAL',
              'X_NUMERIC',
              'X_STR_CATEGORICAL',
              'X_NUMERIC',
              'X_NUMERIC',
              'X_NUMERIC',
              'X_NUMERIC',
              'X_STR_CATEGORICAL',
              'X_NUMERIC',
              'X_NUMERIC',
              'X_NUMERIC',
              'X_NUMERIC']
    ) as sleep_health_model
from sleep_health_group_data
group by group_id
order by group_id

  • 返回结果

    返回值中的sleep_health_model是返回的模型，decisionTreeEncode是模型序列化结果。此函数返回的决策树模型用于decision_tree_predict函数判别该系统对象的分类。

    group_id

    sleep_health_model

    G1

    {
  "returnCode": 0,
  "message": "OK",
  "decisionTreeEncode": "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",
  "decisionTreeInText": "|--- blood_pressure_diastolic \u003c\u003d 93.50\n|   |--- bmi_category.Normal \u003c\u003d 0.50\n|   |   |--- blood_pressure_systolic \u003c\u003d 128.50\n|   |   |   |--- class: None\n|   |   |--- blood_pressure_systolic \u003e  128.50\n|   |   |   |--- daily_steps \u003c\u003d 5600.00\n|   |   |   |   |--- class: Sleep Apnea\n|   |   |   |--- daily_steps \u003e  5600.00\n|   |   |   |   |--- class: Insomnia\n|   |--- bmi_category.Normal \u003e  0.50\n|   |   |--- class: None\n|--- blood_pressure_diastolic \u003e  93.50\n|   |--- class: Sleep Apnea\n",
  "uniqueLabels": [
    "Insomnia",
    "None",
    "Sleep Apnea"
  ],
  "confusionMatrix": [
    [
      120,
      14,
      20
    ],
    [
      8,
      420,
      10
    ],
    [
      2,
      10,
      144
    ]
  ],
  "dataColumnNames": [
    "person_id",
    "gender",
    "age",
    "occupation",
    "sleep_duration",
    "quality_of_sleep",
    "physical_activity_level",
    "stress_level",
    "bmi_category",
    "blood_pressure_systolic",
    "blood_pressure_diastolic",
    "heart_rate",
    "daily_steps",
    "sleep_disorder"
  ],
  "dataColumnTypes": {
    "occupation": "X_STR_CATEGORICAL",
    "blood_pressure_diastolic": "X_NUMERIC",
    "gender": "X_STR_CATEGORICAL",
    "heart_rate": "X_NUMERIC",
    "blood_pressure_systolic": "X_NUMERIC",
    "stress_level": "X_NUMERIC",
    "daily_steps": "X_NUMERIC",
    "physical_activity_level": "X_NUMERIC",
    "bmi_category": "X_STR_CATEGORICAL",
    "sleep_duration": "X_NUMERIC",
    "quality_of_sleep": "X_NUMERIC",
    "sleep_disorder": "Y_STR_CATEGORICAL",
    "age": "X_NUMERIC",
    "person_id": "ID_NUM"
  },
  "categoricalVariableValues": {
    "bmi_category": [
      "Normal",
      "Normal Weight",
      "Obese",
      "Overweight"
    ],
    "gender": [
      "Female",
      "Male"
    ],
    "occupation": [
      "Accountant",
      "Doctor",
      "Engineer",
      "Lawyer",
      "Manager",
      "Nurse",
      "Sales Representative",
      "Salesperson",
      "Scientist",
      "Software Engineer",
      "Teacher"
    ]
  }
}

    G2

    {
  "returnCode": 0,
  "message": "OK",
  "decisionTreeEncode": "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",
  "decisionTreeInText": "|--- blood_pressure_diastolic \u003c\u003d 93.50\n|   |--- bmi_category.Normal \u003c\u003d 0.50\n|   |   |--- blood_pressure_systolic \u003c\u003d 128.50\n|   |   |   |--- class: None\n|   |   |--- blood_pressure_systolic \u003e  128.50\n|   |   |   |--- daily_steps \u003c\u003d 5600.00\n|   |   |   |   |--- class: Sleep Apnea\n|   |   |   |--- daily_steps \u003e  5600.00\n|   |   |   |   |--- class: Insomnia\n|   |--- bmi_category.Normal \u003e  0.50\n|   |   |--- class: None\n|--- blood_pressure_diastolic \u003e  93.50\n|   |--- class: Sleep Apnea\n",
  "uniqueLabels": [
    "Insomnia",
    "None",
    "Sleep Apnea"
  ],
  "confusionMatrix": [
    [
      120,
      14,
      20
    ],
    [
      8,
      420,
      10
    ],
    [
      2,
      10,
      144
    ]
  ],
  "dataColumnNames": [
    "person_id",
    "gender",
    "age",
    "occupation",
    "sleep_duration",
    "quality_of_sleep",
    "physical_activity_level",
    "stress_level",
    "bmi_category",
    "blood_pressure_systolic",
    "blood_pressure_diastolic",
    "heart_rate",
    "daily_steps",
    "sleep_disorder"
  ],
  "dataColumnTypes": {
    "occupation": "X_STR_CATEGORICAL",
    "blood_pressure_diastolic": "X_NUMERIC",
    "gender": "X_STR_CATEGORICAL",
    "heart_rate": "X_NUMERIC",
    "blood_pressure_systolic": "X_NUMERIC",
    "stress_level": "X_NUMERIC",
    "daily_steps": "X_NUMERIC",
    "physical_activity_level": "X_NUMERIC",
    "bmi_category": "X_STR_CATEGORICAL",
    "sleep_duration": "X_NUMERIC",
    "quality_of_sleep": "X_NUMERIC",
    "sleep_disorder": "Y_STR_CATEGORICAL",
    "age": "X_NUMERIC",
    "person_id": "ID_NUM"
  },
  "categoricalVariableValues": {
    "bmi_category": [
      "Normal",
      "Normal Weight",
      "Obese",
      "Overweight"
    ],
    "gender": [
      "Female",
      "Male"
    ],
    "occupation": [
      "Accountant",
      "Doctor",
      "Engineer",
      "Lawyer",
      "Manager",
      "Nurse",
      "Sales Representative",
      "Salesperson",
      "Scientist",
      "Software Engineer",
      "Teacher"
    ]
  }
}

decision_tree_predict函数

结合识别出来的决策树模型和给定的输入数据样本，判别系统对象属于哪一种分类。

varchar decision_tree_predict(decision_tree_model_in_json varchar,input_variable_array array(varchar))

参数

说明

decision_tree_model_in_json

decision_tree_classifier函数的返回值。

input_variable_array

进行判别分类的输入变量数据（一维数组）。

使用示例

  • 查询和分析语句

    * | with model as
(
    select 'G1' as group_id,
        '{"returnCode":0,"message":"OK","decisionTree":{"nodeKey":0,"parentNodeKey":-1,"isLeaf":false,"numSamplesByClass":[124.66666666666676,124.66666666666688,124.66666666666683],"numSamples":374.00000000000045,"probabilitiesByClass":[0.33333333333333315,0.33333333333333354,0.33333333333333337],"predictedClass":"None","predictedClassProbability":0.33333333333333354,"splittingFeature":"blood_pressure_diastolic","threshold":93.5,"depth":1,"leftChild":{"nodeKey":1,"parentNodeKey":0,"isLeaf":false,"numSamplesByClass":[123.04761904761914,121.82039573820417,30.367521367521377],"numSamples":275.2355361533447,"probabilitiesByClass":[0.4470629801925882,0.4426041689265487,0.11033285088086307],"predictedClass":"Insomnia","predictedClassProbability":0.4470629801925882,"splittingFeature":"bmi_category.Normal","threshold":0.5,"depth":2,"leftChild":{"nodeKey":2,"parentNodeKey":1,"isLeaf":false,"numSamplesByClass":[111.7142857142858,17.646879756468795,22.37606837606838],"numSamples":151.737233846823,"probabilitiesByClass":[0.7362351539046778,0.11629894198732474,0.14746590410799743],"predictedClass":"Insomnia","predictedClassProbability":0.7362351539046778,"splittingFeature":"blood_pressure_systolic","threshold":128.5,"depth":3,"leftChild":{"nodeKey":3,"parentNodeKey":2,"isLeaf":true,"numSamplesByClass":[0.0,15.369863013698625,0.0],"numSamples":15.369863013698625,"probabilitiesByClass":[0.0,1.0,0.0],"predictedClass":"None","predictedClassProbability":1.0,"threshold":0.0,"depth":4},"rightChild":{"nodeKey":4,"parentNodeKey":2,"isLeaf":false,"numSamplesByClass":[111.7142857142858,2.2770167427701673,22.37606837606838],"numSamples":136.36737083312434,"probabilitiesByClass":[0.8192156601082596,0.016697665496217574,0.16408667439552274],"predictedClass":"Insomnia","predictedClassProbability":0.8192156601082596,"splittingFeature":"daily_steps","threshold":5600.0,"depth":4,"leftChild":{"nodeKey":5,"parentNodeKey":4,"isLeaf":true,"numSamplesByClass":[14.57142857142857,0.0,20.77777777777778],"numSamples":35.34920634920635,"probabilitiesByClass":[0.41221374045801523,0.0,0.5877862595419848],"predictedClass":"Sleep Apnea","predictedClassProbability":0.5877862595419848,"threshold":0.0,"depth":5},"rightChild":{"nodeKey":6,"parentNodeKey":4,"isLeaf":true,"numSamplesByClass":[97.14285714285721,2.2770167427701673,1.5982905982905984],"numSamples":101.01816448391799,"probabilitiesByClass":[0.9616375197385643,0.022540666368301186,0.015821813893134487],"predictedClass":"Insomnia","predictedClassProbability":0.9616375197385643,"threshold":0.0,"depth":5}}},"rightChild":{"nodeKey":7,"parentNodeKey":1,"isLeaf":true,"numSamplesByClass":[11.333333333333332,104.17351598173533,7.9914529914529915],"numSamples":123.49830230652165,"probabilitiesByClass":[0.09176914274662742,0.8435218463422892,0.06470901091108344],"predictedClass":"None","predictedClassProbability":0.8435218463422892,"threshold":0.0,"depth":3}},"rightChild":{"nodeKey":8,"parentNode Key":0,"isLeaf":true,"numSamplesByClass":[1.619047619047619,2.846270928462709,94.29914529914537],"numSamples":98.7644638466557,"probabilitiesByClass":[0.016393017852670114,0.028818775677068465,0.9547882064702613],"predictedClass":"Sleep Apnea","predictedClassProbability":0.9547882064702613,"threshold":0.0,"depth":2}},"decisionTreeClassLabels":["Insomnia","None","Sleep Apnea"],"decisionTreeEncode":"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\u003d","decisionTreeInText":"|--- blood_pressure_diastolic \u003c\u003d 93.50\n|   |--- bmi_category.Normal \u003c\u003d 0.50\n|   |   |--- blood_pressure_systolic \u003c\u003d 128.50\n|   |   |   |--- class: None\n|   |   |--- blood_pressure_systolic \u003e  128.50\n|   |   |   |--- daily_steps \u003c\u003d 5600.00\n|   |   |   |   |--- class: Sleep Apnea\n|   |   |   |--- daily_steps \u003e  5600.00\n|   |   |   |   |--- class: Insomnia\n|   |--- bmi_category.Normal \u003e  0.50\n|   |   |--- class: None\n|--- blood_pressure_diastolic \u003e  93.50\n|   |--- class: Sleep Apnea\n","uniqueLabels":["Insomnia","None","Sleep Apnea"],"confusionMatrix":[[60,7,10],[4,210,5],[1,5,72]],"dataColumnNames":["person_id","gender","age","occupation","sleep_duration","quality_of_sleep","physical_activity_level","stress_level","bmi_category","blood_pressure_systolic","blood_pressure_diastolic","heart_rate","daily_steps","sleep_disorder"],"expandedColumnNames":["gender.Female","age","occupation.Accountant","occupation.Doctor","occupation.Engineer","occupation.Lawyer","occupation.Manager","occupation.Nurse","occupation.Sales Representative","occupation.Salesperson","occupation.Scientist","occupation.Software Engineer","sleep_duration","quality_of_sleep","physical_activity_level","stress_level","bmi_category.Normal","bmi_category.Normal Weight","bmi_category.Obese","blood_pressure_systolic","blood_pressure_diastolic","heart_rate","daily_steps"],"dataColumnTypes":{"occupation":"X_STR_CATEGORICAL","blood_pressure_diastolic":"X_NUMERIC","gender":"X_STR_CATEGORICAL","heart_rate":"X_NUMERIC","blood_pressure_systolic":"X_NUMERIC","stress_level":"X_NUMERIC","daily_steps":"X_NUMERIC","physical_activity_level":"X_NUMERIC","bmi_category":"X_STR_CATEGORICAL","sleep_duration":"X_NUMERIC","quality_of_sleep":"X_NUMERIC","sleep_disorder":"Y_STR_CATEGORICAL","age":"X_NUMERIC","person_id":"ID_NUM"},"categoricalVariableValues":{"bmi_category":["Normal","Normal Weight","Obese","Overweight"],"gender":["Female","Male"],"occupation":["Accountant","Doctor","Engineer","Lawyer","Manager","Nurse","Sales Representative","Salesperson","Scientist","Software Engineer","Teacher"]},"modelVersion":"1.0.0-20230821"}' as decision_tree_model,
        count(*) as record_count
    from log
),

sleep_health_data as
(
    select 1 as person_id, 'Male' as gender, 27 as age, 'Software Engineer' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 42 as physical_activity_level, 6 as stress_level, 'Overweight' as bmi_category, 126 as blood_pressure_systolic, 83 as blood_pressure_diastolic, 77 as heart_rate, 4200 as daily_steps, 'None' as sleep_disorder
    union all select 2 as person_id, 'Male' as gender, 28 as age, 'Doctor' as occupation, 6.2 as sleep_duration, 6 as quality_of_sleep, 60 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 125 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 75 as heart_rate, 10000 as daily_steps, 'None' as sleep_disorder
    union all select 3 as person_id, 'Male' as gender, 28 as age, 'Doctor' as occupation, 6.2 as sleep_duration, 6 as quality_of_sleep, 60 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 125 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 75 as heart_rate, 10000 as daily_steps, 'None' as sleep_disorder
    union all select 4 as person_id, 'Male' as gender, 28 as age, 'Sales Representative' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Sleep Apnea' as sleep_disorder
    union all select 5 as person_id, 'Male' as gender, 28 as age, 'Sales Representative' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Sleep Apnea' as sleep_disorder
    union all select 6 as person_id, 'Male' as gender, 28 as age, 'Software Engineer' as occupation, 5.9 as sleep_duration, 4 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 85 as heart_rate, 3000 as daily_steps, 'Insomnia' as sleep_disorder
    union all select 7 as person_id, 'Male' as gender, 29 as age, 'Teacher' as occupation, 6.3 as sleep_duration, 6 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Obese' as bmi_category, 140 as blood_pressure_systolic, 90 as blood_pressure_diastolic, 82 as heart_rate, 3500 as daily_steps, 'Insomnia' as sleep_disorder
    union all select 8 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 9 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 10 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 11 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 12 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 7.8 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 13 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6.1 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 14 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 15 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 16 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
    union all select 17 as person_id, 'Female' as gender, 29 as age, 'Nurse' as occupation, 6.5 as sleep_duration, 5 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Normal Weight' as bmi_category, 132 as blood_pressure_systolic, 87 as blood_pressure_diastolic, 80 as heart_rate, 4000 as daily_steps, 'Sleep Apnea' as sleep_disorder
    union all select 18 as person_id, 'Male' as gender, 29 as age, 'Doctor' as occupation, 6 as sleep_duration, 6 as quality_of_sleep, 30 as physical_activity_level, 8 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'Sleep Apnea' as sleep_disorder
    union all select 19 as person_id, 'Female' as gender, 29 as age, 'Nurse' as occupation, 6.5 as sleep_duration, 5 as quality_of_sleep, 40 as physical_activity_level, 7 as stress_level, 'Normal Weight' as bmi_category, 132 as blood_pressure_systolic, 87 as blood_pressure_diastolic, 80 as heart_rate, 4000 as daily_steps, 'Insomnia' as sleep_disorder
    union all select 20 as person_id, 'Male' as gender, 30 as age, 'Doctor' as occupation, 7.6 as sleep_duration, 7 as quality_of_sleep, 75 as physical_activity_level, 6 as stress_level, 'Normal' as bmi_category, 120 as blood_pressure_systolic, 80 as blood_pressure_diastolic, 70 as heart_rate, 8000 as daily_steps, 'None' as sleep_disorder
)

select gm.group_id,
    nid.person_id,
        decision_tree_predict(
            gm.decision_tree_model,
            array[cast(person_id as varchar),
                cast(gender as varchar),
                cast(age as varchar),
                cast(occupation as varchar),
                cast(sleep_duration as varchar),
                cast(quality_of_sleep as varchar),
                cast(physical_activity_level as varchar),
                cast(stress_level as varchar),
                cast(bmi_category as varchar),
                cast(blood_pressure_systolic as varchar),
                cast(blood_pressure_diastolic as varchar),
                cast(heart_rate as varchar),
                cast(daily_steps as varchar)]) as predicted_value
from model as gm
    cross join sleep_health_data as nid
order by gm.group_id, nid.person_id
limit 10000

  • 返回结果

    返回值中的predicted_value是输入样例input_variable_array所属的分类。

    group_id

    person_id

    predicted_value

    G1

    4

    Sleep Apnea

    G1

    5

    Sleep Apnea

    G1

    6

    Sleep Apnea

    ...

    ...

    ...

上一篇：回归分析函数下一篇：负载均衡测量函数
文档推荐