SymetryML6.1
  • Introduction
  • Guides
    • Onboarding Guide
    • Technical Requirements
    • Admin User Guide
    • Installation Guide
      • Installation Guide - GPU
      • Installation Guide - Spark
  • SymetryML GUI
    • ML Toolkit
      • The SymetryML Difference
      • Data Mining Lifecycle
      • SymetryML Concepts
      • Data Sources
      • Streams
      • Encoders
      • Projects
      • Models
    • Sequence Models
    • SymetryML Federated Learning
      • Creating the Federation
      • Load data to local project
      • Requesting Federation Information from Admin Node
      • Joining a Federation with a peer node
      • Federated Data & Modelling
      • Appendix
    • DEM Generator
  • SymetryML Rest Client
    • REST API Reference Guide
      • SymetryML REST API Security
      • SymetryML JSON API Objects
      • Encoder Object REST API
      • SymetryML Projects REST API
      • About Federated Learning
      • Hipaa Compliance and Federated Learning
      • Federated Learning API
        • Federated Learning Topologies
        • Federated Learning with Nats
        • Federated Learning with AWS
        • Fusion Projects
      • Exploration API
      • Modeling API
      • Exporting and Importing Model
      • Third Party Model Rest API
      • SymetryML Job Information
      • Prediction API
      • Data Source API
      • Project Data Source Logs
      • Stream Data Source API
      • AutoML with SymetryML
      • Transform Dataframe
      • Select Model with SymetryML
      • Auto Select with SymetryML
      • Tasks API
      • Miscellaneous API
      • WebSocket API
      • Appendix A JSON Data Structure Schema
      • Appendix B Sample Code
  • SymetryML SaaS
    • SaaS Homepage
    • SaaS Dashboard
    • SaaS Account
    • SaaS Users
    • SaaS Licence
Powered by GitBook
On this page
  • Audience
  • Assumptions
  • Reference Implementation
  • Terminology
  1. SymetryML Rest Client

REST API Reference Guide

PreviousDEM GeneratorNextSymetryML REST API Security

Last updated 2 years ago

This reference manual describes the SymetryML REST Application Programming Interface (API) that is exposed from SymetryML Inc. The API lets you create, update, and delete SymetryML projects that can be used for data-mining exploration and predictive modeling.

Audience

This reference manual is intended for software developers who write client applications that interact with the SymetryML REST API. This reference manual provides documentation of all API operations, which return raw JSON. For optimal utilization of network bandwidth, the SymetryML REST API supports the consumption and creation of JSON data structures only, unless you use a data source from the .

Assumptions

This reference manual assumes that you have a general understanding of RESTful Web Services along with development tools, environments, and IDEs necessary to test and create an application in your programming language of choice. It also ensures that only users with the private key can make requests of those services.

Users of the SymetryML REST service must be authorized by SymetryML Inc.

Reference Implementation

A reference implementation of the rest client for python (pymetry) is available .

Installation of the python script can be performed by download the latest version of pymetry rest client and running the following command:

pip install pymetry-6.1.XXXXX.tar.gz

Terminology

The following table defines key terms related to the SymetryML REST API.

.

Term
Definition

SymetryML Projects

Allow creation and deletion of SymetryML projects, learning and forgetting new data, and obtaining information about SymetryML projects.

Data source

Data sources are abstractions of CSV files that can be accessed through Amazon S3, SFTP, HTTP/HTTPS and other communication APIs.

Encoder

In data mining, the process of transforming a categorical attribute into a continuous attribute is called encoding. Within SymetryML, an encoder object allows the user to transform high-cardinality attributes into meaningful data points that are more predictive than their original form for a particular target attribute

Data exploration

Data mining and statistical exploration of SymetryML projects.

Model building

Allows building models using various modeling algorithms, such as linear discriminant analysis (LDA), multiple linear regression (MLR), and quadratic discriminant analysis (QDA).

Model prediction

Allows to make predictions using models.

Data Source API
here