Training
Our training endpoints allow you to programmatically train models based off of datasets you've uploaded to Akkio. They can be viewed as a better-designed v2 of our legacy /v1/models route.
Training is a fundamentally long operation, so is surfaced through a polling-based mechanism where you make the following calls:
One call to
/newto submit the taskPolling calls to
/{task_id}/statusto check up on your task's statusOne last call to
/{task_id}/resultonce/statusindicates it's done
Model creation via API.
The ID of the dataset to train the model on.
Integer corresponding to how much time we should spend on training. Higher values will take longer but generally be more accurate. Allowed values: 10 (Fastest), 60 (High Quality), 300 (Higher Quality), 1800 (Production)
Helps with predicting rare cases.
falseForces the creation of a new model even if another currently exists.
falseAn array of field names to ignore (case sensitive).
[]An array of field names to predict (case sensitive).
POST /api/v1/models/train/new HTTP/1.1
Host:
Content-Type: application/json
Accept: */*
Content-Length: 160
{
"dataset_id": "text",
"duration": 1,
"extra_attention": false,
"force": false,
"ignore_fields": [
"text"
],
"predict_fields": [
"text"
],
"ANY_ADDITIONAL_PROPERTY": "anything"
}{
"task_id": "text",
"ANY_ADDITIONAL_PROPERTY": "anything"
}Retrieves the status of the provided model creation call.
GET /api/v1/models/train/{task_id}/status HTTP/1.1
Host:
Accept: */*
{
"metadata": {
"type": "PENDING",
"ANY_ADDITIONAL_PROPERTY": "anything"
},
"status": "PENDING",
"ANY_ADDITIONAL_PROPERTY": "anything"
}Retrieves the result of a model creation call.
GET /api/v1/models/train/{task_id}/result HTTP/1.1
Host:
Accept: */*
{}Example Call Sequence​
Here's a rough outline of how you'd make a request to the Inference bulk predictions endpoints.
HTTP Headers​
1. Create Request​
First, we'll submit the task into our asynchronous processing queue.
POST /api/v1/models/train/new
{
"dataset_id": "YTV32jCdVf5DbcMxzvX5",
"predict_fields": ["Positive Lead"]
}You'll receive an object like this containing a task id:
{
"task_id": "<task_id>"
}We'll use this in the next request.
2. Query for Task Status​
Next, we'll query the status endpoint at a cadence to see whether the task is complete yet. This request might look something like this:
GET /api/v1/models/train/<task_id>/status
Note that you must use the same Task ID that you received from the task creation endpoint above.
This will provide you with a status field set to either SUBMITTED, IN_PROGRESS, FAILED, or SUCCEEDED. You can read more about each state on the Asynchronous Endpoints page.
Here's an example response you might get:
{
"status": "IN_PROGRESS",
"metadata": {
"type": "IN_PROGRESS"
}
}You should retry ("poll") this endpoint at a regular cadence until you get a response that looks something like this:
{
"status": "SUCCEEDED",
"metadata": {
"type": "SUCCEEDED",
"location": "/api/v1/models/train/<task_id>/result"
}
}note
The location field is always relative to the API root (https://api.akkio.com/api/v1), not the overall website root (https://api.akkio.com). You'll need to remember to construct the end URL from the site name, API root, and the provided location.
Armed with this information, we'll move to the last request.
3. Query for Result​
Armed with the location we got from the status call, we'll make a request for the end result.
GET /api/v1/models/train/<task_id>/result
You'll get a response that looks something like this:
{
"status": "success",
"model_id": "AyooDGG4IB7iJDkgLKJR",
"stats": [
[
{
"field": 12,
"field_name": "Positive Lead",
"field_type": "category",
"class": 0,
"class_name": "0",
"count": 19,
"true positives": 19,
"false positives": 0,
"false negatives": 0,
"precision": 1.0,
"recall": 1.0,
"f1": 1.0,
"frequency": 0.95
},
{
"field": 12,
"field_name": "Positive Lead",
"field_type": "category",
"class": 1,
"class_name": "1",
"count": 1,
"true positives": 1,
"false positives": 0,
"false negatives": 0,
"precision": 1.0,
"recall": 1.0,
"f1": 1.0,
"frequency": 0.05
}
]
],
"field_importance": {
"Job Title": 0.09528297930955887,
"Years of Experience": 0.09974530339241028,
"Company Size": 0.08637341856956482,
"Industry": 0.08510678261518478,
"Location": 0.050445690751075745,
"Webinars Attended": 0.1286168396472931,
"Whitepapers Downloaded": 0.10489732772111893,
"Pages Visited": 0.08934492617845535,
"Days Since Last Visit": 0.07931949943304062,
"Email Open Rate": 0.08199360221624374,
"Email Click Rate": 0.05456322059035301,
"Responded to Survey": 0.04431045055389404,
"Positive Lead": 8.27786672541464e-10
},
"data_story": [
{
"name": "Positive Lead",
"type": "category",
"outcomes": [
{
"outcome": "0",
"causes": [
{
"field": "Webinars Attended",
"top_value": "3",
"bottom_value": "3"
},
{
"field": "Whitepapers Downloaded",
"top_value": "3",
"bottom_value": "7"
},
{
"field": "Years of Experience",
"top_value": "6",
"bottom_value": "7"
},
{
"field": "Job Title",
"top_value": "Executive",
"bottom_value": "Assistant"
},
{
"field": "Pages Visited",
"top_value": "27",
"bottom_value": "1"
}
],
"top_case": 1.0,
"avg_case": 0.94,
"bottom_case": 1.0
},
{
"outcome": "1",
"causes": [
{
"field": "Webinars Attended",
"top_value": "3",
"bottom_value": "3"
},
{
"field": "Whitepapers Downloaded",
"top_value": "7",
"bottom_value": "3"
},
{
"field": "Years of Experience",
"top_value": "7",
"bottom_value": "6"
},
{
"field": "Job Title",
"top_value": "Assistant",
"bottom_value": "Executive"
},
{
"field": "Pages Visited",
"top_value": "1",
"bottom_value": "27"
}
],
"top_case": 0.0,
"avg_case": 0.06,
"bottom_case": 0.0
}
]
}
]
}Last updated
Was this helpful?