curl --request POST \
--url https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}/clean \
--header 'Authorization: Bearer <token>'import requests
url = "https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}/clean"
headers = {"Authorization": "Bearer <token>"}
response = requests.post(url, headers=headers)
print(response.text){
"datasetKey": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"status": "PENDING",
"datasetCreationProgressUrl": "<string>",
"numberOfBuckets": 123,
"datasetUploadInfo": {
"uploadUrl": "<string>",
"extraHeaders": "<string>"
},
"uploadTargets": [
{
"kind": "TRAIN",
"url": "<string>",
"requiredHeader": "<string>"
}
]
}Clean the dataset
Prepares an uploaded dataset for training. Returns immediately; the dataset moves to
PROCESSING. Poll GET /api/v1/datasets/{datasetKey} for the outcome.
- Call it only once both input files have been uploaded.
- Call it once. The artefacts a training run reads are built one time.
- Do not call it on a dataset created from a refinement. That route starts the work itself.
A 409 means one of two things, and the message says which: an input has not arrived, or the
dataset was already cleaned.
curl --request POST \
--url https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}/clean \
--header 'Authorization: Bearer <token>'import requests
url = "https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}/clean"
headers = {"Authorization": "Bearer <token>"}
response = requests.post(url, headers=headers)
print(response.text){
"datasetKey": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"status": "PENDING",
"datasetCreationProgressUrl": "<string>",
"numberOfBuckets": 123,
"datasetUploadInfo": {
"uploadUrl": "<string>",
"extraHeaders": "<string>"
},
"uploadTargets": [
{
"kind": "TRAIN",
"url": "<string>",
"requiredHeader": "<string>"
}
]
}Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Path Parameters
Response
Cleaning started.
^[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[4][0-9a-fA-F]{3}-[89aAbB][0-9a-fA-F]{3}-[0-9a-fA-F]{12}$Status of dataset creation:
PENDING- The dataset has been created and is pending further actions. Mainly data additionPROCESSING- The dataset is currently being processed. When this status is returned an url to check status is returned tooCOMPLETED- The dataset has been processed and is completed. The dataset is now ready to be used for training modelsFAILED- The dataset processing has failed
PENDING, PROCESSING, COMPLETED, FAILED Where to PUT the CSV on the single-file route. Prefer uploadTargets, which names each file it
expects.
uploadUrl is a signed URL valid for 60 minutes. PUT the CSV as a binary body with
Content-Type: text/csv and every header listed in extraHeaders (one colon-joined
Name:value pair per header, semicolon-separated). Send no Authorization header: the signature
covers the request, and an extra header breaks it.
Show child attributes
Show child attributes
Where to PUT each input file. A dataset built from a train and a labelled test file returns one target per file; a dataset whose files came from a completed refinement returns none, because nothing is uploaded. Empty on the single-file pipeline.
Show child attributes
Show child attributes