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.
Size is not checked before cleaning starts. The train file needs at least 25 labelled rows for the
least common target value, counted after the split. A dataset below that moves to ERROR rather than
COMPLETED, with the reason in errors, and cleaning the same files again will not change the
outcome.
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