Approve a proposal and start materialization
curl --request POST \
--url https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"edits": {
"remove_features": [
"<string>"
],
"rename": {},
"label": {
"name": "label",
"type": "binary",
"column": "<string>",
"derivation": "<string>",
"positive_class_value": "<unknown>"
},
"notes": "<string>"
},
"auto_train": false,
"train_request": {
"source_table": "<string>",
"label_column": "<string>",
"feature_columns": [
"<string>"
]
}
}
'import requests
url = "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve"
payload = {
"edits": {
"remove_features": ["<string>"],
"rename": {},
"label": {
"name": "label",
"type": "binary",
"column": "<string>",
"derivation": "<string>",
"positive_class_value": "<unknown>"
},
"notes": "<string>"
},
"auto_train": False,
"train_request": {
"source_table": "<string>",
"label_column": "<string>",
"feature_columns": ["<string>"]
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
edits: {
remove_features: ['<string>'],
rename: {},
label: {
name: 'label',
type: 'binary',
column: '<string>',
derivation: '<string>',
positive_class_value: '<unknown>'
},
notes: '<string>'
},
auto_train: false,
train_request: {
source_table: '<string>',
label_column: '<string>',
feature_columns: ['<string>']
}
})
};
fetch('https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'edits' => [
'remove_features' => [
'<string>'
],
'rename' => [
],
'label' => [
'name' => 'label',
'type' => 'binary',
'column' => '<string>',
'derivation' => '<string>',
'positive_class_value' => '<unknown>'
],
'notes' => '<string>'
],
'auto_train' => false,
'train_request' => [
'source_table' => '<string>',
'label_column' => '<string>',
'feature_columns' => [
'<string>'
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve"
payload := strings.NewReader("{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"training_table": "<string>",
"training_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"message": "<string>"
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}Feature engineering
Approve a proposal and start materialization
Apply edits, mark approved, and queue the CTAS materialization.
POST
/
api
/
automl
/
feature-engineering
/
jobs
/
{job_id}
/
approve
Approve a proposal and start materialization
curl --request POST \
--url https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"edits": {
"remove_features": [
"<string>"
],
"rename": {},
"label": {
"name": "label",
"type": "binary",
"column": "<string>",
"derivation": "<string>",
"positive_class_value": "<unknown>"
},
"notes": "<string>"
},
"auto_train": false,
"train_request": {
"source_table": "<string>",
"label_column": "<string>",
"feature_columns": [
"<string>"
]
}
}
'import requests
url = "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve"
payload = {
"edits": {
"remove_features": ["<string>"],
"rename": {},
"label": {
"name": "label",
"type": "binary",
"column": "<string>",
"derivation": "<string>",
"positive_class_value": "<unknown>"
},
"notes": "<string>"
},
"auto_train": False,
"train_request": {
"source_table": "<string>",
"label_column": "<string>",
"feature_columns": ["<string>"]
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
edits: {
remove_features: ['<string>'],
rename: {},
label: {
name: 'label',
type: 'binary',
column: '<string>',
derivation: '<string>',
positive_class_value: '<unknown>'
},
notes: '<string>'
},
auto_train: false,
train_request: {
source_table: '<string>',
label_column: '<string>',
feature_columns: ['<string>']
}
})
};
fetch('https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'edits' => [
'remove_features' => [
'<string>'
],
'rename' => [
],
'label' => [
'name' => 'label',
'type' => 'binary',
'column' => '<string>',
'derivation' => '<string>',
'positive_class_value' => '<unknown>'
],
'notes' => '<string>'
],
'auto_train' => false,
'train_request' => [
'source_table' => '<string>',
'label_column' => '<string>',
'feature_columns' => [
'<string>'
]
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve"
payload := strings.NewReader("{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/api/automl/feature-engineering/jobs/{job_id}/approve")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"edits\": {\n \"remove_features\": [\n \"<string>\"\n ],\n \"rename\": {},\n \"label\": {\n \"name\": \"label\",\n \"type\": \"binary\",\n \"column\": \"<string>\",\n \"derivation\": \"<string>\",\n \"positive_class_value\": \"<unknown>\"\n },\n \"notes\": \"<string>\"\n },\n \"auto_train\": false,\n \"train_request\": {\n \"source_table\": \"<string>\",\n \"label_column\": \"<string>\",\n \"feature_columns\": [\n \"<string>\"\n ]\n }\n}"
response = http.request(request)
puts response.read_body{
"job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"training_table": "<string>",
"training_job_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"message": "<string>"
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"error": "<string>",
"code": 500
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>",
"input": "<unknown>",
"ctx": {}
}
]
}Authorizations
OAuth2AuthorizationCodeBearerAPIKeyHeader
The access token received from the authorization server in the OAuth 2.0 flow.
Path Parameters
Feature engineering job ID.
Body
application/json
Request body for approving a proposal and starting materialization.
Edits the user wants applied to the recipe before materialization.
Show child attributes
Show child attributes
If true, kick off AutoML training once materialization completes.
Training request to forward when auto_train is true.
Show child attributes
Show child attributes
Response
Successful Response
Response returned by the approval endpoint.
Lifecycle of an AutoML feature engineering job.
Available options:
running, pending_approval, failed, approved, materializing, complete Target table queued for materialization.
AutoML training job ID if auto_train chained successfully.
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