Python

import os
from woodwide import WoodWide

client = WoodWide(
    api_key=os.environ.get("WOOD_WIDE_AI_API_KEY"),  # This is the default and can be omitted
)
response = client.api.models.clustering.infer(
    model_id="model_id",
)
print(response)

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

API

Run Clustering Inference

Assigns cluster labels to the input dataset using a trained clustering model. The model must be in COMPLETE status. Returns the cluster assignments for each instance. By default, attempts to coerce the given dataset into the input schema required by the model (e.g., adding missing required columns and populating them with null values). Set coerce_schema=False to prevent schema coercion and enforce strict validation. When coerce_schema=False, the system specifically expects that the required input columns associated with the model are a subset of the columns of the dataset and that the types match.

POST

api

models

clustering

{model_id}

infer

Python

import os
from woodwide import WoodWide

client = WoodWide(
    api_key=os.environ.get("WOOD_WIDE_AI_API_KEY"),  # This is the default and can be omitted
)
response = client.api.models.clustering.infer(
    model_id="model_id",
)
print(response)

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>"
    }
  ]
}

Authorizations

Authorization

string

header

required

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

Path Parameters

model_id

string | null

required

Query Parameters

dataset_id

string | null

dataset_name

string | null

model_name

string | null

Body

application/x-www-form-urlencoded

coerce_schema

boolean

default:true

Response

Successful Response

Run Prediction Inference Run Anomaly Detection

⌘I

API Reference

Authentication

API

Run Clustering Inference

Authorizations

Path Parameters

Query Parameters

Body

Response