Funciones de análisis de clasificación - Simple Log Service - Centro de documentación de Alibaba Cloud

Los modelos de aprendizaje automático de clasificación identifican las clases de los objetos del sistema en tiempo real; por ejemplo, detectan solicitudes de ataque o descubren relaciones entre elementos. A continuación se describen la sintaxis y los ejemplos de las funciones de análisis de clasificación.

Contexto

Los ejemplos de funciones de clasificación utilizan el siguiente índice de muestra. Para obtener más información, consulte Create indexes.

El índice de muestra contiene los siguientes 14 campos: person_id (long, ID del paciente), gender (text, género), age (long, edad), occupation (text, ocupación), sleep_duration (double, duración del sueño), quality_of_sleep (long, calidad del sueño), physical_activity_level (long, nivel de actividad física), stress_level (long, nivel de estrés), bmi_category (text, categoría de IMC), blood_pressure_systolic (long, presión arterial sistólica), blood_pressure_diastolic (long, presión arterial diastólica), heart_rate (long, frecuencia cardíaca), daily_steps (long, pasos diarios) y sleep_disorder (text, trastorno del sueño). La función statistics está habilitada para todos los campos.

El siguiente código muestra el registro de muestra:

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

Funciones

Las funciones de análisis de clasificación utilizan un modelo de aprendizaje automático para determinar la clase de un objeto del sistema en tiempo real.

Función

Sintaxis

Descripción

Tipo de dato del valor devuelto

decision_tree_classifier function

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

)

Devuelve un modelo de árbol de decisión entrenado para clasificar datos y analizar causas a partir de las muestras especificadas.

varchar

decision_tree_predict function

decision_tree_predict(

decision_tree_model_in_json varchar,

input_variable_array array(varchar)

)

Identifica las clases de los objetos del sistema con base en las muestras especificadas y el modelo de árbol de decisión que devuelve la función decision_tree_classifier.

varchar

Función decision_tree_classifier

La función decision_tree_classifier devuelve un modelo de árbol de decisión entrenado para clasificar datos y analizar causas a partir de las muestras especificadas.

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)

Parámetro	Descripción
`target_variable`	Variable de salida.
`input_variable_array`	Arreglo de variables de entrada. Cada variable debe convertirse a cadena.
`target_variable_name`	Nombre de la variable de salida.
`input_variable_name_array`	Arreglo de nombres de las variables de entrada.
`input_variable_type_array`	Arreglo de tipos de las variables de entrada. Tipos de variables de entrada compatibles: `ID_STR`: identificador de tipo cadena para el objeto de clasificación. `ID_NUM`: identificador de tipo numérico para el objeto de clasificación. `X_STR_CATEGORICAL`: datos discretos de tipo cadena. Por ejemplo, los valores válidos de un campo son Male, Female y Unknown. `X_NUM_CATEGORICAL`: datos discretos de tipo numérico. Por ejemplo, los valores válidos de un campo son 1, 2, 3, 4 y 5. `X_NUMERIC`: datos continuos de tipo numérico.
`model_options`	Opcional. Parámetros avanzados especificados como una cadena de pares clave-valor separados por comas o punto y coma. Este parámetro no suele ser obligatorio. Por ejemplo: `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`. Parámetros avanzados del modelo de árbol de decisión: `criterion`: criterios para dividir nodos. Establezca el valor en `gini`, `entropy` o `log_loss`. `splitter`: directiva utilizada para dividir un nodo. Establezca el valor en `best` o `random`. `max_depth`: profundidad máxima del árbol de decisión. `min_samples_split`: número mínimo de muestras necesarias para dividir un nodo. `min_samples_leaf`: número mínimo de muestras requeridas en un nodo hoja. `min_weight_fraction_leaf`: proporción mínima del peso de la muestra respecto al peso total de las muestras. `max_leaf_nodes`: número máximo de nodos hoja. `min_impurity_decrease`: disminución de impureza necesaria para dividir un nodo. `class_weight`: peso de la clase. Si se establece en `balanced`, se compensa el desequilibrio de muestras al hacer que los pesos sean inversamente proporcionales a la cantidad de muestras por clase. Entre más muestras tenga una clase, menor será el peso por muestra.

Ejemplo

Instrucción de consulta

* | 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         -- Add a group_id field to show that decision_tree_classifier acts as an aggregate function.
  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

Resultados de la consulta y el análisis

El campo sleep_health_model contiene el modelo de árbol de decisión. El campo decisionTreeEncode contiene el modelo de árbol de decisión serializado. Puede usar este modelo en la función decision_tree_predict para identificar la clase de un objeto del sistema.

group_id

sleep_health_model

{
  "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"
    ]
  }
}

{
  "returnCode": 0,
  "message": "OK",
  "decisionTreeEncode": "gANjc2tsZWFybi50cmVlLl9jbGFzc2VzCkRlY2lzaW9uVHJlZUNsYXNzaWZpZXIKcQApgXEBfXECKFgJAAAAY3JpdGVyaW9ucQNYBAAAAGdpbmlxBFgIAAAAc3BsaXR0ZXJxBVgEAAAAYmVzdHEGWAkAAABtYXhfZGVwdGhxB05YEQAAAG1pbl9zYW1wbGVzX3NwbGl0cQhLFFgQAAAAbWluX3NhbXBsZXNfbGVhZnEJSSpYGAAAAG1pbl93ZWlnaHRfZnJhY3Rpb25fbGVhZnEKRwAAAAAAAAAAWAwAAABtYXhfZmVhdHVyZXNxC05YDgAAAG1heF9sZWFmX25vZGVzcQxOWAwAAAByYW5kb21fc3RhdGVxDU5YFQAAAG1pbl9pbXB1cml0eV9kZWNyZWFzZXEORz+EeuFHrhR7WBIAAABtaW5faW1wdXJpdHlfc3BsaXRxD05YDAAAAGNsYXNzX3dlaWdodHEQWAgAAABiYWxhbmNlZHERWAkAAABjY3BfYWxwaGFxEkcAAAAAAAAAAFgOAAAAbl9mZWF0dXJlc19pbl9xE0sXWAsAAABuX2ZlYXR1cmVzX3EUSxdYCgAAAG5fb3V0cHV0c19xFUsBWAgAAABjbGFzc2VzX3EWY251bXB5LmNvcmUubXVsdGlhcnJheQpfcmVjb25zdHJ1Y3QKcRdjbnVtcHkKbmRhcnJheQpxGEsAhXEZQwFicRqHcRtScRwoSwFLA4VxHWNudW1weQpkdHlwZQpxHlgDAAAAVTExcR+JiIdxIFJxIShLA1gBAAAAPHEiTk5OSyxLBEsIdHEjYolDhEkAAABuAAAAcwAAAG8AAABtAAAAbgAAAGkAAABhAAAAAAAAAAAAAAAAAAAATgAAAG8AAABuAAAAZQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABTAAAAbAAAAGUAAABlAAAAcAAAACAAAABBAAAAcAAAAG4AAABlAAAAYQAAAHEkdHElYlgKAAAAbl9jbGFzc2VzX3EmY251bXB5LmNvcmUubXVsdGlhcnJheQpzY2FsYXIKcSdoHlgCAAAAaThxKImIh3EpUnEqKEsDaCJOTk5K/////0r/////SwB0cStiQwgDAAAAAAAAAHEshnEtUnEuWA0AAABtYXhfZmVhdHVyZXNfcS9LF1gFAAAAdHJlZV9xMGNza2xlYXJuLnRyZWUuX3RyZWUKVHJlZQpxMUsXaBdoGEsAhXEyaBqHcTNScTQoSwFLAYVxNWgqiUMIAwAAAAAAAABxNnRxN2JLAYdxOFJxOX1xOihoB0sEWAoAAABub2RlX2NvdW50cTtLCVgFAAAAbm9kZXNxPGgXaBhLAIVxPWgah3E+UnE/KEsBSwmFcUBoHlgDAAAAVjU2cUGJiIdxQlJxQyhLA1gBAAAAfHFETihYCgAAAGxlZnRfY2hpbGRxRVgLAAAAcmlnaHRfY2hpbGRxRlgHAAAAZmVhdHVyZXFHWAkAAAB0aHJlc2hvbGRxSFgIAAAAaW1wdXJpdHlxSVgOAAAAbl9ub2RlX3NhbXBsZXNxSlgXAAAAd2VpZ2h0ZWRfbl9ub2RlX3NhbXBsZXNxS3RxTH1xTShoRWgeWAIAAABpOHFOiYiHcU9ScVAoSwNoIk5OTkr/////Sv////9LAHRxUWJLAIZxUmhGaFBLCIZxU2hHaFBLEIZxVGhIaB5YAgAAAGY4cVWJiIdxVlJxVyhLA2giTk5OSv////9K/////0sAdHFYYksYhnFZaEloV0sghnFaaEpoUEsohnFbaEtoV0swhnFcdUs4SwFLEHRxXWKJQvgBAAABAAAAAAAAAAgAAAAAAAAAFAAAAAAAAAAAAAAAAGBXQIxVVVVVVeU/7AIAAAAAAAAjAAAAAGCHQAIAAAAAAAAABwAAAAAAAAAQAAAAAAAAAAAAAAAAAOA/Os7p5i3y4j9qAgAAAAAAAFi6jsHEM4FAAwAAAAAAAAAEAAAAAAAAABMAAAAAAAAAAAAAAAAQYEDSdORBSg3bP+QAAAAAAAAAZbNva5f3ckD//////////////////////v////////8AAAAAAAAAwAAAAAAAAAAANgAAAAAAAADdq1evXr0+QAUAAAAAAAAABgAAAAAAAAAWAAAAAAAAAAAAAAAA4LVAtALg68NO0z+uAAAAAAAAALo4eoDBC3FA//////////////////////7/////////AAAAAAAAAMBsIUDNeQPfPywAAAAAAAAAzbIsy7KsUUD//////////////////////v////////8AAAAAAAAAwBCB6oIYErM/ggAAAAAAAAAXGF6bKUFpQP/////////////////////+/////////wAAAAAAAADAemDLWrmn0T+GAQAAAAAAAK+BWy/k325A//////////////////////7/////////AAAAAAAAAMCIcoA8/1e2P4IAAAAAAAAABRfF+eywaEBxXnRxX2JYBgAAAHZhbHVlc3FgaBdoGEsAhXFhaBqHcWJScWMoSwFLCUsBSwOHcWRoV4lD2LGqqqqqKm9AUaqqqqoqb0CYqqqqqipvQDcMwzAMw25A81YgXYF0bkDeFV7hFV5OQOG2bdu27WtAXnpm6ZmlQUAERmAERmBGQAAAAAAAAAAA3atXr169PkAAAAAAAAAAAOG2bdu27WtAdSPVjVQ3EkAERmAERmBGQEySJEmSJD1AAAAAAAAAAAByHMdxHMdEQJckSZIkSWhAdSPVjVQ3EkAqmZIpmZIJQKyqqqqqqjZAebjG4hoLakBzP/dzP/cvQHqe53me5wlAU2xKsSnFFkAsJVMyJZNnQHFldHFmYnViWBAAAABfc2tsZWFybl92ZXJzaW9ucWdYBgAAADAuMjQuMnFodWIu",
  "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"
    ]
  }
}

Función decision_tree_predict

La función decision_tree_predict identifica las clases de los objetos del sistema con base en las muestras especificadas y el modelo de árbol de decisión devuelto.

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

Parámetro	Descripción
`decision_tree_model_in_json`	Valor devuelto por la decision_tree_classifier function.
`input_variable_array`	Arreglo de variables de entrada utilizadas para la clasificación. La función convierte las variables de entrada en un arreglo unidimensional.

Ejemplo

Instrucción de consulta

* | 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

Resultados de la consulta y el análisis

El campo predicted_value indica la clase a la que pertenecen las variables de entrada en input_variable_array.

group_id

person_id

predicted_value

G1

4

Sleep Apnea

G1

5

Sleep Apnea

G1

6

Sleep Apnea

...

...

...

group_id	person_id	predicted_value
G1	4	Sleep Apnea
G1	5	Sleep Apnea
G1	6	Sleep Apnea
...	...	...