Learn frame data
Learns from a raw `DataFrame` posted inline (as opposed to `dss/{dsName}/learn`, which learns from a registered data source via an always-async job). Set `async=true` to run as a background job — the response is `202` with the job id in the `sym-job-id` header and an empty body; poll `GET /{user}/jobs/{jobId}` for completion. Without `async` (or `async=false`), learning happens synchronously and the `200` response body has empty `values` — it is not a job-status payload.
Learns from a raw DataFrame posted inline (as opposed to
dss/{dsName}/learn, which learns from a registered data source via
an always-async job). Set async=true to run as a background job —
the response is 202 with the job id in the sym-job-id header and
an empty body; poll GET /{user}/jobs/{jobId} for completion.
Without async (or async=false), learning happens synchronously
and the 200 response body has empty values — it is not a
job-status payload.
Authorization
SymetryMLAuth HMAC-SHA256 signature-based authentication: requests are signed with
your secret key and sent with the Customer-ID, Sym-date,
Authorization, Content-MD5 and sym-version headers.
See SymetryML REST API Security
for the signature algorithm and a complete example.
In: header
Path Parameters
User/Customer identifier
Project identifier
Query Parameters
Request Body
application/json
TypeScript Definitions
Use the request body type in TypeScript.
Response Body
application/json
application/json
application/json
application/json
curl -X POST "https://example.com/c1/projects/nbra_prj_iris/learn" \ -H "Content-Type: application/json" \ -d '{ "attributeNames": [ "sepal_length", "sepal_width", "petal_length", "petal_width", "Iris_setosa" ], "attributeTypes": [ "C", "C", "C", "C", "B" ], "data": [ [ "5.1", "3.5", "1.4", "0.2", "1" ], [ "4.9", "3.0", "1.4", "0.2", "1" ], [ "6.2", "3.4", "5.4", "2.3", "0" ] ] }'{ "statusCode": 200, "statusString": "OK", "values": {}}{ "statusCode": 202, "statusString": "Job Created", "values": {}}{ "statusCode": 400, "statusString": "No types specified in DataFrame.", "values": {}}{ "statusCode": 500, "statusString": "Generic Throwable [com.google.gson.JsonSyntaxException: com.google.gson.stream.MalformedJsonException: Unterminated object at line 1 column 21 path $.evil_hack]", "values": {}}Forget frame data POST
Forgets a raw `DataFrame` posted inline — the inverse of `learn`. Set `async=true` to run as a background job (`202`, job id in the `sym-job-id` header, empty body); without it, forgetting happens synchronously and the `200` response body has empty `values`. Not supported on `sequence`-type projects.
Auto-select optimal model POST
Auto-select the optimal model for a given task (binary classifier or regression). Under the hood, SymetryML builds many different models across various permutations of the selected input attributes, and also considers various model types for the given task, then chooses the best one. The request body is an `MLContext` object.