curl --request GET \
--url https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey} \
--header 'Authorization: Bearer <token>'import requests
url = "https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}"
headers = {"Authorization": "Bearer <token>"}
response = requests.get(url, headers=headers)
print(response.text){
"datasetKey": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"datasetName": "<string>",
"status": "PENDING",
"createdOn": "<string>",
"updatedOn": "<string>",
"numberOfBuckets": 123,
"inputDataDownloadUrl": "<string>",
"metadata": {
"numberOfColumns": 123,
"numberOfRows": 123,
"numberOfClasses": 123,
"titlesOfDataColumns": "<string>",
"columnTypes": "<string>",
"detectedTargetClassLabels": "<string>",
"numberOfFeaturesPerClass": "<string>",
"columnsWithUniqueValues": "<string>",
"columnsWithTooManyFeatures": "<string>"
},
"errors": {
"errors": [
"<string>"
]
},
"warnings": {
"warnings": [
"<string>"
]
},
"inputs": [
{
"kind": "TRAIN",
"uploaded": true
}
],
"inputDatasource": "<string>",
"featureRefinementKey": "<string>",
"uploadedTrainRowCount": 123,
"trainRowCount": 123,
"recommendedThreshold": 123,
"recommendedNumberOfBuckets": 123
}Retrieve dataset details
Retrieve dataset details by dataset key
curl --request GET \
--url https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey} \
--header 'Authorization: Bearer <token>'import requests
url = "https://{tenant}-api.mathfi.ai/api/v1/datasets/{datasetKey}"
headers = {"Authorization": "Bearer <token>"}
response = requests.get(url, headers=headers)
print(response.text){
"datasetKey": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"datasetName": "<string>",
"status": "PENDING",
"createdOn": "<string>",
"updatedOn": "<string>",
"numberOfBuckets": 123,
"inputDataDownloadUrl": "<string>",
"metadata": {
"numberOfColumns": 123,
"numberOfRows": 123,
"numberOfClasses": 123,
"titlesOfDataColumns": "<string>",
"columnTypes": "<string>",
"detectedTargetClassLabels": "<string>",
"numberOfFeaturesPerClass": "<string>",
"columnsWithUniqueValues": "<string>",
"columnsWithTooManyFeatures": "<string>"
},
"errors": {
"errors": [
"<string>"
]
},
"warnings": {
"warnings": [
"<string>"
]
},
"inputs": [
{
"kind": "TRAIN",
"uploaded": true
}
],
"inputDatasource": "<string>",
"featureRefinementKey": "<string>",
"uploadedTrainRowCount": 123,
"trainRowCount": 123,
"recommendedThreshold": 123,
"recommendedNumberOfBuckets": 123
}Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Path Parameters
^[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}$Response
Dataset details successfully retrieved
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 Show child attributes
Show child attributes
Show child attributes
Show child attributes
Show child attributes
Show child attributes
The dataset's input files and which are still missing — so a client can say which upload is outstanding rather than only that the dataset is not ready. Empty on the single-file pipeline.
Show child attributes
Show child attributes
Where this dataset's inputs came from: UPLOAD when the caller uploaded them, FEATURE_REFINERY when they came from a completed refinement, CLOUD_STORAGE on the older single-file route. Settled when the dataset is created and never changes afterwards, so a client can decide once. In particular it is what says whether the refinement's column filter means anything here — it is offered only on a dataset that came from a refinement.
The refinement these inputs came from, when they did. Null otherwise. Present so a client can link back to the run without keeping its own record of which one was chosen. An opaque handle: pass it back as given rather than parsing it.
How many training rows were uploaded. Absent on a dataset built before the count was kept.
The number of training rows the dataset was prepared from.
The performance target a training on this dataset should start from — the threshold the refinement it came from trained to. Absent when the files were uploaded directly, in which case the client's own default applies.
The bucket count this dataset was founded at, when that came from a refinement. Absent on a direct upload, where the caller chose it. On this route it cannot be changed: the refined files were produced at this bucket count.