Start a training run on a dataset

curl --request POST \
  --url https://{tenant}-api.mathfi.ai/api/v1/training/datasets/{datasetKey} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "scalingFactor": 19,
  "performanceThreshold": 0.85
}
'

import requests

url = "https://{tenant}-api.mathfi.ai/api/v1/training/datasets/{datasetKey}"

payload = {
    "scalingFactor": 19,
    "performanceThreshold": 0.85
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

"{\n \"trainingJobKey\": \"41eac90e-de55-4d60-8b10-8a57ee27db2e\",\n \"datasetKey\": \"7215eec1-f233-419e-bad2-8fda560dff75\",\n  \"status\": \"PENDING\"\n }\n"

Training

Start a training run on a dataset

Start a Crucible run against a dataset. The dataset must be COMPLETED.

The run trains many attempts at once across three algorithm families, scores each on the 20% test data, ranks them by fewest wrong decisions, and keeps the best three as v1, v2 and v3 of one model. v1 becomes the champion; POST /api/v1/training/{trainingJobKey}/champion moves it.

Two settings:

performanceThreshold — the accuracy each attempt aims for, between 0 and 1. Set this to the dataset’s recommendedThreshold when it has one. A dataset that came from a refinement carries the target that refinement actually reached; a target far above it makes every attempt run to its time cap, and the run can settle on a model that predicts one class for every row. Where there is no recommendedThreshold, start near what the data plausibly supports rather than at 0.9.
scalingFactor — an integer, 0 to 499. 19 is the working default and rarely needs changing.

Returns immediately. Poll GET /api/v1/training/{trainingJobKey}/progress until the status is terminal, honouring nextPollAfterSeconds.

POST

/

api

/

v1

/

training

/

datasets

/

{datasetKey}

Start a training run on a dataset

curl --request POST \
  --url https://{tenant}-api.mathfi.ai/api/v1/training/datasets/{datasetKey} \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "scalingFactor": 19,
  "performanceThreshold": 0.85
}
'

import requests

url = "https://{tenant}-api.mathfi.ai/api/v1/training/datasets/{datasetKey}"

payload = {
    "scalingFactor": 19,
    "performanceThreshold": 0.85
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

"{\n \"trainingJobKey\": \"41eac90e-de55-4d60-8b10-8a57ee27db2e\",\n \"datasetKey\": \"7215eec1-f233-419e-bad2-8fda560dff75\",\n  \"status\": \"PENDING\"\n }\n"

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

datasetKey

string<uuid>

required

Body

application/json

scalingFactor

integer<int32>

required

Required range: 0 <= x <= 499

Example:

19

performanceThreshold

number<double>

required

Read this off the dataset's recommendedThreshold where it has one, rather than choosing a round number. See the endpoint description.

Required range: 0 <= x <= 1

Example:

0.70336

Response

Training job successfully created

trainingJobKey

string<uuid>

required

datasetKey

string<uuid>

required

status

enum<string>

required

Status of training:

PENDING - The training job has been created and is pending execution
RUNNING - The training job is currently executing and progressing
COMPLETED - The training job has completed execution successfully, reaching the target performance. A model has been generated.
TIMED_OUT - The training job could not reach the target performance in the configured
CANCELLED - The training job has been cancelled by the user
NOT_COMPLETED - The training job has stalled without making progress in a specified timeframe
FAILED - The training job has failed due to an error

Available options:

PENDING,

RUNNING,

COMPLETED,

TIMED_OUT,

CANCELLED,

FAILED,

NOT_COMPLETED