Model Generate

The model_generate session, is an implementation that generates a desired model, using previously uploaded dataset(s), via either the data_new, or the data_append session. The required attributes, for the corresponding session, is sent to the /load-data endpoint, which is demonstrated as follows:

• svm example
• svr example

Note: the content of each of the above examples, can be substituted for the data attribute, in a given POST request:

import requests

endpoint = 'https://192.168.99.101:9090/load-data'
headers = {
    'Authorization': 'Bearer ' + token,
    'Content-Type': 'application/json'
}

requests.post(endpoint, headers=headers, data=json_string_here)

Note: more information, regarding how to obtain a valid token, can be further reviewed, in the documentation.

The following properties define the above data attribute:

• collection: collection of dataset documents, used to generate a model
• session_type: corresponds to one of the following session types:
  • data_new
  • data_append
  • model_generate
  • model_predict
• model_type: the type of model to perform on:
  • svm
  • svr
• sv_kernel_type: the type of kernel to apply to the support vector model_type:
  • linear
  • polynomial
  • rbf
  • sigmoid
• penalty: optional float, of the error term
• gamma: optional float, kernel coefficient for rbf, poly, and sigmoid
  • if set to auto, then 1/n_features will be used